Article
To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Although SVM performs well, still there is a difficulty in identifying the normal and abnormal events. Yannick et al. [13] used low-level features for event modelling and abnormal event detection. In the first phase, the co-occurrence matrix is obtained using the spatio-temporal attributes of normal events. ...
... This approach is mainly focused on context modelling approach to govern the activity zones trajectory. However, these statistical approaches [6,13,14] are more suitable for identifying the abnormalities and they have not considered the motion estimation by interrelations among the nearer objects. Sangmin et al. [16] proposed the spatio-temporal discriminator to determine whether the given video sequences are in normal or abnormal. ...
... Thus, it is verified that the nearest-neighbour search recognises the test data as abnormal based on proposed SOOF features and clearly presents in Figs. [12][13][14]. Abnormal detection rate is estimated and represented in percentage by finding the number of times the abnormal events such as wrong side driving, pedestrian crossing the road illegally is found over the total number of abnormalities present in the test video. ...
Article
Full-text available
Detection of abnormal events in the traffic scene is very challenging and is a significant problem in video surveillance. The authors proposed a novel scheme called super orientation optical flow (SOOF)‐based clustering for identifying the abnormal activities. The key idea behind the proposed SOOF features is to efficiently reproduce the motion information of a moving vehicle with respect to superorientation motion descriptor within the sequence of the frame. Here, the authors adopt the mean absolute temporal difference to identify the anomalies by motion block (MB) selection and localisation. SOOF features obtained from MB are used as motion descriptor for both normal and abnormal events. Simple and efficient K‐means clustering is used to study the normal motion flow during the training. The abnormal events are identified using the nearest‐neighbour searching technique in the testing phase. The experimental outcome shows that the proposed work is effectively detecting anomalies and found to give results better than the state‐of‐the‐art techniques.
... With the development of artificial intelligence, this problem can be resolved using automated anomaly detectors [1][2][3]. Extensive works including supervised models [5][6][7][8][9][10] and unsupervised models [15,16,18,22,24] have been proposed in recent years [4]. ...
... A more complex idea than learning a threshold is to train a multidimensional model of normal events [8][9][10]. Benezeth et al. [8] created a co-occurrence matrix indicating when and where pixels had active motion labels. ...
... A more complex idea than learning a threshold is to train a multidimensional model of normal events [8][9][10]. Benezeth et al. [8] created a co-occurrence matrix indicating when and where pixels had active motion labels. They learned a normal co-occurrence matrix by a Markov Random Field (MRF). ...
... With the development of artificial intelligence, this problem can be resolved using automated anomaly detectors [1,2,3]. Extensive works including supervised models [5,6,7,8,9,10] and unsupervised models [15,16,18,22,24] have been proposed in recent years [4]. ...
... A more complex idea than learning a threshold is to train a multidimensional 90 model of normal events [8,9,10]. Benezeth et al. [8] created a co-occurrence matrix indicating when and where pixels had active motion labels. ...
... A more complex idea than learning a threshold is to train a multidimensional 90 model of normal events [8,9,10]. Benezeth et al. [8] created a co-occurrence matrix indicating when and where pixels had active motion labels. They learned a normal co-occurrence matrix by a Markov Random Field (MRF). ...
Article
Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the behavior patterns far away from our model are labeled as anomalous, for which far away is measured by a time-varying threshold. Experiments are implemented on three surveillance datasets, namely UMN, UCSD Ped1/Ped2 and Avenue dataset. All datasets have changing scenes due to mutable crowd density and behavior types. Anomaly detection results show that our model can adapt to the current scene rapidly and reduce false alarms while still detecting most anomalies. Quantitative comparisons with 12 recent approaches further confirm our superiority.
... We can further categorize dominant behaviors into two classes. In the literature on human attention processes, the rst usually deals with foreground activities in [9,8,30,49] while the second describes the scene background 1 . Typically, the detection of the latter is more restrictively referred to as background subtraction, which is the building block of almost all computer vision algorithms. ...
... This approach diers from conventional background subtraction and salient point detection methods. 8 1.2 EVENT UNDERSTANDING: PROBLEM STATEMENTS AND CONTRIBUTIONS ...
... This is achieved by constructing a hierarchical BOW algorithm that learns both dominant and abnormal events in a unied framework. More closely related to our proposed approach are those methods that construct a spatio-temporal behavioral model of the scene [49,8,48,30]. To date, these have focused on detecting low-level local anomalies in a video by analyzing the activity pattern of each pixel as a function of time. ...
Thesis
Full-text available
This thesis focuses on monitoring non-specific and unconstrained activities and events in videos in order to build a complete scene understanding system. The particular emphasis in this work is based on the spatio-temporal context of the scene. This thesis proposes a unique solution using a hierarchical framework of video fragments to create a dynamically changing model of the scene. The model is then used to simultaneously detect and localize an event of interest, detect abnormal (rare) events, and track all moving objects in the scene. The approach can be considered as an extension to the original Bag-of-Video-Words approaches in which a spatio-temporal scene configuration comes into play. It does not require prior knowledge about actions and events, background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. The hierarchical algorithm uses a probabilistic framework to code a video as a compact set of local spatio-temporal visual features, while considering their spatio-temporal compositions in order to account for the scene context. A significant aspect of the methodology is the way that we represent scene information while keeping the computational cost low enough for real-time implementation using the current hardware resources. Given the adaptive appearance- and motion-based model, the events can be described and localized in the videos. These events are interpreted by a complete scene understanding system that uses different inference mechanisms and learning strategies to describe ongoing events in a video, identify abnormal patterns is space and time, find similar videos to a query based on their contents, and track all moving objects in the scene without using any object detection method. We have extensively tested all our system on popular benchmarks and shown that they are both effective and robust for all of the aforementioned tasks. Moreover, the results are highly competitive with state-of-the-art methods. However, a major advantage of our approach is that it does not require any feature analysis, background/foreground segmentation and tracking, and is susceptible to on-line real-time analysis.
... Pixel-based abstraction methods include histograms of spatio-temporal gradients [Zelnik-Manor 2006]; spatio-temporal patches [Dollár 2005, Laptev 2007, Niebles 2008, Haines 2011, Kim 2009, Benezeth 2011, Benezeth 2009, Bregler 1997, Wang 2006]; self-similarity surfaces ]; motion history images (MHI) motion energy images (MEI) and pixel change history (PCH) [Bobick 2001, Zhong 2004, Ng 2001, Kosmopoulos 2010, Jiménez-Hernández 2010, Bradski 2002; optical flow [Utasi 2010, Utasi 2008a, Utasi 2008b, Kwak 2011, Adam 2008, Varadarajan 2009]; middle-level feature consisting of serval patches [Boiman 2007] (please refer to details of middle-level feature in , Doersch 2012). ...
... In [Benezeth 2011, Benezeth 2009], an approach using spatio-temporal models of scenes was presented. A Markov random field model parameterized by a co-occurrence matrix was built. ...
Thesis
One of the major research areas in computer vision is visual surveillance. The scientific challenge in this area includes the implementation of automatic systems for obtaining detailed information about the behavior of individuals and groups. Particularly, detection of abnormal individual movements requires sophisticated image analysis. This thesis focuses on the problem of the abnormal events detection, including feature descriptor design characterizing the movement information and one-class kernel-based classification methods. In this thesis, three different image features have been proposed: (i) global optical flow features, (ii) histograms of optical flow orientations (HOFO) descriptor and (iii) covariance matrix (COV) descriptor. Based on these proposed descriptors, one-class support vector machines (SVM) are proposed in order to detect abnormal events. Two online strategies of one-class SVM are proposed: The first strategy is based on support vector description (online SVDD) and the second strategy is based on online least squares one-class support vector machines (online LS-OC-SVM)
... A Normal Behavior Model [16] was proposed for behavior modeling and abnormal event detection by using low level features. This method was directly processed with event characterization and behavior modeling using low level features. ...
...  Because of sparsity of scenes, the performance of [15] is quite low.  When dealing with moving objects in videos, [16] used a threshold value which highly influences the performance.  The [17] concentrated only on global events instead of local ones which lead to worse detection rate. ...
... To detect certain types of exceptional events; Mehran et al. [34] argue that the model of social work is the best solution to detect and locate aberrant behavior in certain videos of the crowd. To solve the measurement of this problem of anomalies, most conventional algorithms [1,3] detect the test sample based on the low probability as an anomaly by fitting a model of training data. In addition, several other algorithms such as the Hidden Markov Model (HMM) [1], the Markov Random Field (MRF) [2] and Temporal MRF [3] have been proposed. ...
... To solve the measurement of this problem of anomalies, most conventional algorithms [1,3] detect the test sample based on the low probability as an anomaly by fitting a model of training data. In addition, several other algorithms such as the Hidden Markov Model (HMM) [1], the Markov Random Field (MRF) [2] and Temporal MRF [3] have been proposed. ...
Article
Full-text available
In this paper, two new methods are developed in order to detect and track unexpected events in scenes. The process of detecting people may face some difficulties due to poor contrast, noise and the small size of the defects. For this purpose,the perfect knowledge of the geometry of these defects is an essential step in assessing the quality of detection. First, we collected statistical models of the element for each individual for time tracking of different people using the technique of Gaussian mixture model (GMM). Then we improved this method to detect and track the crowd(IGMM). Thereafter, we adopted two methods: the differential method of Lucas and Kanade(LK) and the method of optical flow estimation of Horn Schunck(HS) for optical flow representation. Then, we proposed a novel descriptor, named the Distribution of Magnitude of Optical Flow (DMOF) for anomalous events’ detection in the surveillance video. This descriptor represents an algorithm whose aim is to accelerate the action of abnormal events’ detection based on a local adjustment of the velocity field by manipulating the light intensity.
... A new framework based on real-time to detect the traffic accidents using Histogram of Flow Gradient and logistic regression modeling have proposed in Sadeky et al. (2010), Sadek et al. (2010). Benezeth et al. (2011) developed a method for abnormal activity detection using low-level features. In this, illegal U-turns of the vehicle and dropped abandoned baggage have been detected by using co-occurrence matrix and statistical model can be estimated from training video sequence. ...
... Violence detection approaches Jiang et al. (2011) Spatial and temporal context based Viterbi Algorithm with HMM Detection rate is more than 90% for point anomaly, more than 80% for sequential anomaly, and more than 70% for co-occurrence anomaly Benezeth et al. (2011) Low-level feature based Markov Random Field accounts for direction, speed and size without any intervention Proposed detected all the abnormal activities with 9.5% false positives Wiliem et al. (2012) Contextual information based Color, shape and flame movements information based ...
Article
Full-text available
Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.
... Recently, the focus of anomaly detection has turned from object detection or tracking to local spatiotemporal features. Several approaches have been proposed and received increasing attention [32,33]. Typically, these approaches focus on pixel level. ...
... But they are not able to handle online and real-time detection because they are highly time-consuming and computationally expensive [40]. In addition, several approaches [32,35,43,44] try to construct models based on the spatiotemporal behavior and analyse the spatiotemporal pattern of each pixel as a function of time to detect lowlevel local anomalous events. However, they ignore the relationship between each pixel in space and time because they just process each pixel independently such as in [43], which may lead to too local detection. ...
Article
Full-text available
Automatically discovering anomalous events and objects from surveillance videos plays an important role in real-world application and has attracted considerable attention in computer vision community. However it is still a challenging issue. In this paper, a novel approach for automatic anomaly detection is proposed. Our approach is highly efficient; thus it can perform real-time detection. Furthermore, it can also handle multiscale detection and can cope with spatial and temporal anomalies. Specifically, local features capturing both appearance and motion characteristics of videos are extracted from spatiotemporal video volume (STV). To bridge the large semantic gap between low-level visual feature and high-level event, we use the middle-level visual attributes as the intermediary. And these three-level framework is modeled as an extreme learning machine (ELM). We propose to use the spatiotemporal pyramid (STP) to capture the spatial and temporal continuity of an anomalous even, enabling our approach to cope with multiscale and complicated events. Furthermore, we propose a method to efficiently update the ELM; thus our approach is self-adaptive to background change which often occurs in real-world application. Experiments on several datasets are carried out and the superior performance of our approach compared to the state-of-the-art approaches verifies its effectiveness.
... HMM feature is used to determine local motion pattern that is based on the 3D Gaussian distribution. A Spatio-temporal model is also implemented in [30], where abnormal activities in the speed, size and direction of object were detected. A video surveillance system is developed in [31][32] for abnormal visual detection and recognition in crowd. ...
Article
Full-text available
p>The manuscript discusses about abnormalities in a crowded scenario. To prevent the mishap at a public place, there is no much mechanism which could prevent or alert the concerned authority about suspects in a crowd. Usually in a crowded scene, there are chances of some mishap like a terrorist attack or a crime. Our target is finding techniques to identify such activities and to possibly prevent them. If the crowd members exhibit abnormal behavior, we could identify and say that this particular person is a suspect and then the concerned authority would look into the matter. There are various methods to identify the abnormal behavior. The proposed approach is based on optical flow model. It has an ability to detect the sudden changes in motion of an individual among the crowd. First, the main region of motion is extracted by the help of motion heat map. Harris corner detector is used for extracting point of interest of extracted motion area. Based on the point of interest an optical flow is estimated here. After analyzing this optical flow model, a threshold value is fixed. Basically optical flow is an energy level of individual frame. The threshold value is forwarded to SVM classifier, which produces a better result with 99.71% accuracy. This approach is very useful in real time video surveillance system where a machine can monitor unwanted crowd activity. </p
... In Refs. [29][30][31][32], the authors model motion patterns with histograms of pixel changes. In Refs. ...
Article
Full-text available
It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, the PCAnet is trained to extract high-level semantics of the crowd’s situation. Next, kPCA, a one-class classifier, is trained to identify anomalies within the scene. In contrast to some prevailing deep learning methods, this framework is completely self-supervised because it utilizes only video sequences of a normal situation. Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota (UMN dataset) and Anomaly Detection dataset from University of California, San Diego (UCSD dataset), and competitive results that yield a better equal error rate (EER) and area under curve (AUC) than other state-of-the-art methods are observed. Furthermore, by adding a local response normalization (LRN) layer, we propose an improvement to the original AED-Net. The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.
... In [24], researchers propose a feature descriptor, covariance matrix, which encodes optical flow and partial derivatives of adjacent frames. In [25][26][27][28], authors model motion patterns with histograms of pixel changes. In [29][30][31], distributions of optical flow are used as the basic features, and then models for detecting abnormal events are built based on optic flow features. ...
Preprint
Full-text available
It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.
... The detection of both normal (e.g., [1][2][3][4][5][6][7][8][9][10][11][12]) and abnormal (e.g., [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]) video events is a cardinal chore of a surveillance camerasystem. An automated camera system can provide goodtrajectories of objects. ...
... The detection of both normal (e.g., [1][2][3][4][5][6][7][8][9][10][11][12]) and abnormal (e.g., [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]) video events is a cardinal chore of a surveillance camerasystem. An automated camera system can provide goodtrajectories of objects. ...
Article
Full-text available
Laser scanners have a lot of advantages over conventional video cameras. Data processing in laser scanner system becomes faster and easier because there is no need to record real world videos. Besides, the problem of private life conservation is taken away. This paper proposes an approach to track objects from laser scanned dataset. Data points collected by each scan of laser scanners are considered as video frame. Support vector machine (SVM) along with Hungarian algorithm and Kalman filter are used to obtain trajectories of objects from the laser scanned dataset. Experimental results on the same laser scanned dataset show that the method of SVM with Hungarian algorithm and Kalman filter performs better than that of its alternative having various thresholds with Hungarian algorithm and Kalman filter.
... In [36], by analyzing the trajectory, the abnormal event was detected. As the trajectory of the object is hard to be detected due to the occlusion, other feature based methods without trajectory extraction, such as motion history images (MHI), motion energy images (MEI), pixel change history (PCH) [2,22,24] were used to present the movement from the original images. In [55], the motion feature, namely expanded relative motion histogram of bag-ofvisual-words (ERMH-BoW) was proposed for event detection. ...
Article
Full-text available
Abnormal event detection is one of the most important objectives in security surveillance for public scenes. In this paper, a new high-performance algorithm based on spatio-temporal motion information is proposed to detect global abnormal events from the video stream as well as the local abnormal event. We firstly propose a feature descriptor to represent the movement by adopting the covariance matrix coding optical flow and the corresponding partial derivatives of multiple connective frames or the patches of the frames. The covariance matrix of multi-RoI (region of interest) which consists of frames or patches can represent the movement in high accuracy. For public surveillance video, the normal samples are abundant while there are few abnormal samples. Thus the one-class classification method is suitable for handling this problem inherently. The nonlinear one-class support vector machine based on a proposed kernel for Lie group element is applied to detect abnormal events by merely training the normal samples. The computational complexity and time performance of the proposed method is analyzed. The PETS, UMN and UCSD benchmark datasets are employed to verify the advantages of the proposed method for both global abnormal and local abnormal event detection. This method can be used for event detection for a surveillance video and outperforms the state-of-the-art algorithms. Thus it can be adopted to detect the abnormal event in the monitoring video.
... Yannick Benezeth describes a method for behavior modeling and abnormal events detection which uses low level features. In conventional objectbased approaches [7] objects are identified, classified, and tracked to locate those with suspicious behavior. The co-occurrence matrix is thus used for detecting moving objects whose behavior differs from the ones observed during the training phase. ...
... In Benezeth et al. (2011), co-occurrence matrices for key pixels are embedded in a Markov random field formulation to describe the probability of abnormalities. Zhong et al. (2004) also uses co-occurrence matrices, but in an unsupervised setting. ...
Article
Full-text available
We propose an original method for detecting and localizing anomalous motion patterns in videos from a camera view-based motion representation perspective. Anomalous motion should be taken in a broad sense, i.e., unexpected, abnormal, singular, irregular, or unusual motion. Identifying distinctive dynamic information at any time point and at any image location in a sequence of images is a key requirement in many situations and applications. The proposed method relies on so-called labeled affine flows (LAF) involving both affine velocity vectors and affine motion classes. At every pixel, a motion class is inferred from the affine motion model selected in a set of candidate models estimated over a collection of windows. Then, the image is subdivided in blocks where motion class histograms weighted by the affine motion vector magnitudes are computed. They are compared blockwise to histograms of normal behaviors with a dedicated distance. More specifically, we introduce the local outlier factor (LOF) to detect anomalous blocks. LOF is a local flexible measure of the relative density of data points in a feature space, here the space of LAF histograms. By thresholding the LOF value, we can detect an anomalous motion pattern in any block at any time instant of the video sequence. The threshold value is automatically set in each block by means of statistical arguments. We report comparative experiments on several real video datasets, demonstrating that our method is highly competitive for the intricate task of detecting different types of anomalous motion in videos. Specifically, we obtain very competitive results on all the tested datasets: 99.2% AUC for UMN, 82.8% AUC for UCSD, and 95.73% accuracy for PETS 2009, at the frame level.
... Another widely used approach (see [17], [26], [27], [28], [29], [30], [31] and [32]) to the detection of abnormal behavior in trajectories is modeling their spatio-temporal dynamics probabilistically as activities using hidden Markov models (HMM). In the following a closer look is taken on two of the HMM approaches. ...
Article
Full-text available
In Chapter 2, fusion-based decentralized low penetration traffic state estimation systems (DFCD) are described, ranging from Class B (WLAN) to Class B (BT) assisting low penetration Class C (V2X) vehicles. Afterwards, Chapter 3 introduces and evaluates further decentralized surveillance applications, such as traffic Anomalies and Incident Detection (AID) as well as Local Emission Monitoring systems (EMS). Chapter 4 summarizes the described work.
... Other approaches simply classify the objects that are not grouped into clusters, representing the outliers, as abnormal [5]- [7], or detect objects that move in different speed or direction as unusual events [8]. In this class of work, there are two main categories: trajectory-based [9] and pixel-based [10]- [12] approaches. ...
Article
In this paper, we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory-based or pixel-based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different data sets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared with existing approaches.
... [19]) flag abnormal events based on independent location-specific statistical models and have not considered the relationships between local observations. Benezeth et al. [2] used a Markov Random Fields (MRF) model parameterized by a co-occurrence matrix to allow for spatial consistency detection. Real-time constrains are another pursuit in [1,13]. ...
Conference Paper
Full-text available
This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression. While local anomaly is typically detected as a 3D pattern matching problem, we are more interested in global anomaly that involves multiple normal events interacting in an unusual manner such as car accident. To simultaneously detect local and global anomalies, we formulate the extraction of normal interactions from training video as the problem of efficiently finding the frequent geometric relations of the nearby sparse spatio-temporal interest points. A codebook of interaction templates is then constructed and modeled using Gaussian process regression. A novel inference method for computing the likelihood of an observed interaction is also proposed. As such, our model is robust to slight topo-logical deformations and can handle the noise and data un-balance problems in the training data. Simulations show that our system outperforms the main state-of-the-art methods on this topic and achieves at least 80% detection rates based on three challenging datasets.
... Consistent detection, which integrates the potentially local scores from a detector into a globally consistent mask, is beneficial in locating action targets. For anomaly detection, Benezeth et al. [21] used an Markov Random Fields (MRF) model parameterized by a co-occurrence matrix to allow for spatial consistency detection. Li et al. [22] used the conditional random field (CRF) to synthesize the scores derived from multi-scale spatial/temporal detectors. ...
Article
Full-text available
This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the authors' best knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.
... The local motion patterns with 3D Gaussian distributions are captured via a HMM. In [19], a spatio-temporal model also was used. Abnormal activities in the direction, speed and size of objects were detected. ...
Article
Full-text available
In this paper, we propose a novel algorithm based on the acceleration feature to detect anomalous crowd behaviors in video surveillance systems. Different from the previous work that uses independent local feature, the algorithm explores the global moving relation between the current behavior state and the previous behavior state. Due to the unstable optical flow resulting in the unstable speed, a new global acceleration feature is proposed, based on the gray-scale invariance of three adjacent frames. It can ensure the pixels matching and reflect the change of speed accurately. Furthermore, a detection algorithm is designed by acceleration computation with a foreground extraction step. The proposed algorithm is independent of the human detection and segmentation, so it is robust. For anomaly detection, this paper formulates the abnormal event detection as a two-classified problem, which is more robust than the statistic model-based methods, and this two-classified detection algorithm, which is based on the threshold analysis, detects anomalous crowd behaviors in the current frame. Finally, apply the method to detect abnormal behaviors on several benchmark data sets, and show promising results.
Article
Full-text available
Multimedia anomaly datasets play a crucial role in automated surveillance. They have a wide range of applications expanding from outlier objects/ situation detection to the detection of life-threatening events. For more than 1.5 decades, this field has attracted a lot of research attention, and as a result, more and more datasets dedicated to anomalous actions and object detection have been developed. Tapping these public anomaly datasets enable researchers to generate and compare various anomaly detection frameworks with the same input data. This paper presents a comprehensive survey on a variety of video, audio, as well as audio-visual datasets based on the application of anomaly detection. This survey aims to address the lack of a comprehensive comparison and analysis of multimedia public datasets based on anomaly detection. Also, it can assist researchers in selecting the best available dataset for bench-marking frameworks. Additionally, we discuss gaps in the existing dataset and insights for future direction towards developing multimodal anomaly detection datasets.
Thesis
Full-text available
Civil security is the set of methods implemented by a State or an organization to protect civilian populations, as well as their property and activities, in times of war, crisis, and peace, against risks or threats of any kind. Moreover, it consists of ensuring the safety of people against all types of natural risks such as fires or against various threats that could endanger their lives, as well as that of their property or activities (acts of terrorism, acts of vandalism, etc.). In recent years, the use of drones for surveillance tasks has been on the rise worldwide. So, The number of cameras that must be analyzed increases and the efficiency and accuracy of human operators have reached their limits. Moreover, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a deep learning method in unsupervised mode to solve this problem becomes fundamental. In this thesis, we have proposed many deep learning architectures capable of detecting abnormal events with high performance.
Preprint
Full-text available
Multimedia anomaly datasets play a crucial role in automated surveillance. They have a wide range of applications expanding from outlier object/ situation detection to the detection of life-threatening events. This field is receiving a huge level of research interest for more than 1.5 decades, and consequently, more and more datasets dedicated to anomalous actions and object detection have been created. Tapping these public anomaly datasets enable researchers to generate and compare various anomaly detection frameworks with the same input data. This paper presents a comprehensive survey on a variety of video, audio, as well as audio-visual datasets based on the application of anomaly detection. This survey aims to address the lack of a comprehensive comparison and analysis of multimedia public datasets based on anomaly detection. Also, it can assist researchers in selecting the best available dataset for bench-marking frameworks. Additionally, we discuss gaps in the existing dataset and future direction insights towards developing multimodal anomaly detection datasets.
Chapter
Full-text available
Hematopoietic stem cell transplantation (HSCT) is a standard therapeutic intervention for hematological malignancies and several monogenic diseases where using therapeutic cloning technique the defective gene is reproduced via insertion of new gene. However, this approach has limitations and few drawbacks regarding to the lack of a suitable donor, graft-versus-host disease and infectious and severe complications due to immune suppression and autoimmune response. Though it shows good efficacy, earlier gene therapy trials with autologous Hematopoietic stem cells (HSCs) and viral vectors have given birth to serious health safety concerns. Targeted gene-editing technologies using genetically engineered nucleases such as Zinc finger nuclease (ZFN) or TALEN or CRISPR/Cas9 shows great clinical promise and good efficacy, allowing for the site-specific repair of mutations that causing mutation by a process with important applications in autosomal dominant or dominant negative genetic disorders. The relative simplicity and ease of the CRISPR/Cas9 system, in particular, has marked an exponential increase in the research community’s interest in and use of these genome editing technologies. HSPCs are more effective but challenging to the targets; the specific mechanisms of evolution of the cells is to protect themselves from DNA damage render them potentially more susceptible to oncogenesis, especially given their ability of self renewal and their proliferative potential for a long-term condition. This new inventive technology finds a new way of manipulation of genes for investigation of gene functions and effectiveness during hematopoiesis, as well as for the stability and repair of the genetic mutations in HSC transplantation–based clinical therapies for diseases such as sickle cell disease, β-thalassemia, and primary immunodeficiency.
Book
Full-text available
1. Evaluation of Tourism Services and its Impact on Andhra Pradesh Tourism with Special Reference to Rayalaseema Region - A Case Study on Aptdc ................................................................................... 1 – Dr. J. Venkatesh, M. Prasanthi 2. Role of Young Entreprenuers and the Recent Trends in Entrepreneurship Activities in India...........11 – Dr. J. Venkatesh, Dr. R. Lavanya Kumari...................................................................................................... 3. Wireless Network Technology for Process Automation.......................................................................... 17 – Dr. M. S. Gowtham, Dr. S. Syed Jamaesha 4. Reliable Energy Aware Multi-Hop Routing for Wsn............................................................................. 25 – Dr. S.gopinath, Mr. S. Pragadeswaran 5. Consumer Behaviour in Present Marketing Scenario............................................................................ 33 – Prof. Kumar Ratnesh 6. Why Higher Variability in Trading Volume Results in Lower Expected Returns............................... 44 – Vibhore 7. Just in Time Leadership............................................................................................................................. 54 – Shikha Kansal 8. Impact of Block-Chain On E-Commerce : A Scot Analysis.................................................................... 64 – Dr. Palvinder Kaur Bakshi 9. Human Resource Management - Big Five Personality Traits................................................................ 70 – Shemi Varghese 10. Management & Sustainable Development............................................................................................... 82 – Dr. Mrinalini Pathak 11. Empowerment of Women in India............................................................................................................ 91 – Dr. Ravi Kumar Gupta 12. Smart and Eco Cities................................................................................................................................ 100 – Dr. K.kavita, Dr. Jyoti Shinde 13. Green Chemical Technology for Industrial Applications......................................................................113 – Diksha Chaudhary, Soumava Santra & Tanay Pramanik 14. Corporate Governance .............................................................................................................................119 – Neha Kousar 15. Role of Optimization Techniques in Welding......................................................................................... 125 – M. Sowrirajan, S. Vijayan, M. Arulraj, V. Rajkumar 16. Work from Home Outlasts Covid-19...................................................................................................... 141 – Dr. Ramachandra Arya 17. Modeling and Solving Large Scale Optimization Problems Using Spreadsheets............................... 157 – Dr Thothathri Venugopal 18. Production of Succinic Acid from Agricultural Residues..................................................................... 163 – Nidhi Tripathi 19. Metagenomics: A New Approach............................................................................................................ 180 – Gibarni Mahata 20. Crispr/Cas9 Genome Editing on Human Hematopoietic Stem Cell.................................................... 186 – Pratik Chatterjee 21. Lung Cancer Detection And Diagnosis Using the Machine Learning Approach............................... 192 – Nandini Gupta & Pratik Chatterjee 22. A Brief on Artificial Intelligence.............................................................................................................. 200 – Aarushi Sharma 23. Renewable Energy Sources: A Need for Sustainable Development..................................................... 204 – Megha Bhatnagar 24. Overview of Financial Management and Modern Finance Practices.................................................. 210 – Sajal Basu Bosch 25. Higher Alcohol as a Fuel Additive in Spark Ignition Engines.............................................................. 219 – Dr. Chandrakant B. Kothare 26. Plastic Waste Management...................................................................................................................... 226 – Dr. Piyush 27. Cluster, Cloud and Grid Computing...................................................................................................... 234 – Rohit Raja, Manisha Achantani, Richa Sharma 28. Environmental Sustainability and Water Management....................................................................... 244 – Pradipta Kumar Sarangi 29. Renewable Energy System for Sustainable Development..................................................................... 255 Dr. Shivendra 30. Traffic Management Using IoT............................................................................................................... 266 – Neha Tyagi, Sushant Jhingran 31. Nanoplastics in Drinking Water.............................................................................................................. 278 – Akshata Mandloi, Dr. Manishita Das Mukherji 32. Role of Pseudogene in Diagnosis and treatment of Cancer.................................................................. 289 – Sachin Goel, Mansi Agrahari 33. Artificial Intelligence and Machine Learning in Cyber Security and Threat Intelligence................ 296 – Amrita 34. Overview of Marketing Management & Modern HRM Practices..................................................... 305 – Sajal Kanti Basu 35. Soft Computing Techniques in Solar Energy......................................................................................... 319 – Dr. Hoor Fatima, Neha Tyagi 36. Overview of Human Resource Management & Modern Marketing Practices.................................. 331 – Sajal Kanti Basu 37. Behavioural Analysis for Suspicious Character in Crowd................................................................... 336 – Neha Tyagi, Preeti Dubey, Amit Kumar Upadhyay 38. Importance of Sustainable Citizen to Achieve Sustainability............................................................... 345 – Meenakshi Bisla 39. Future Needs, a Society's Dream and Challenge for Industry............................................................. 345 – Shailender Gaur 40. Solid Waste Management......................................................................................................................... 360 – Dr. Piyush Gupta
Chapter
One of the most interesting and important open issues in the automated video surveillance community is the analysis of human activities which requires a higher level of understanding. A framework was introduced for the prediction of human activity using temporal sequence patterns. In this framework, Sequential Pattern Mining (SPM) converted the complex symbolic sequence in the video into frequent itemsets using the Apriori algorithm. Apriori algorithm has to scan the video multiple times to find the frequent itemset that results in high execution time and memory consumption problems. To solve these problems, various association mining algorithms such as Rapid Association Rule Mining (RARM), Equivalence class clustering, and bottom-up lattice traversal (Eclat), diffset Eclat (dEclat) and Frequent Pattern-growth (FP-growth) algorithms are introduced for mining frequent itemsets. RARM speeds up the mining process at the minimum support threshold by using the Support-Ordered Trie Itemset (SOTrieIT) tree structure. The performance of frequent itemset mining is enhanced by using Eclat where depth-first approach is accommodated in it to find the frequent itemsets. However, the pruning technique in Eclat is more difficult. So, a dEclat algorithm is used where a depth-first search is performed to find frequent itemsets. But, dEclat is not more suitable for the sparse database. So, an FP-growth algorithm is introduced where a compressed data structure called FP-tree is used that solves the time and memory problem of the Apriori algorithm. The mined frequent itemsets are normal human behavior and the remaining activities are abnormal human activity. The experiments are carried out to prove the effectiveness of the RARM, Eclat, dEclat, and FP-growth for mining frequent activities in the video.
Conference Paper
Automated abnormal detection system meets the need of society for detecting and locating anomalies and alerting the operators. In this paper, we proposed a constrained self-adaptive sparse combination representation (CSCR). The spatio-temporal video volumes low-level features, which be stacked with multi-scale pyramid, can extract features effectively. The CSCR strategy is robust to learn dictionary and detect abnormal behaviors. Experiments on the published dataset and the comparison to other existing methods demonstrate the certain advantages of our method.
Article
A novel abnormity detection method is presented which combines the low-rank structured sparse representation and reduced dictionary learning. The multi-scale three-dimensional gradient is used as low-level feature by encoding the spatiotemporal information. A group of reduced sparse dictionaries is learnt by low-rank approximation based on the structured sparsity property of the video sequence. The contribution of this study is three-fold: (i) the normal feature clusters can be represented effectively by the reduced dictionaries which are learnt based on the low-rank nature of the data; (ii) the size of dictionary is determined adaptively by the sparse learning method according to the scene, which makes the representation more compact and efficient; and (iii) the proposed abnormity detection method is of low time complexity and real-time detection can be obtained. The authors have evaluated the proposed method against the state-of-the-art methods on the public datasets and very promising results have been achieved.
Article
Full-text available
Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.
Chapter
Crimes such as theft, violence against people, damage to property, etc., have become quite common in a society, which a serious concern. The traditional surveillance systems act like post mortem tools in the sense that they can be used for the investigation to detect the person behind the theft, but it is only after the crime has already occurred. In this chapter, we propose a method for automatically detecting the suspicious or violent activities of a person from the surveillance video. We train the SVM classifier with the HOG features extracted from the video frames of two types: frames showing no violent activities and those showing violent activities like kicking, pushing, punching, etc. In the testing phase, the frames from the surveillance video are read and processed in order to classify them as violent or normal frames. If the frames classified as violent frames are detected, an alarm is raised to alert the controller. It can be used to keep track of the time duration for which a person is found loitering at a place being monitored. If the time exceeds a predefined threshold, the alarm is raised to alert about any potential suspicious activity so that it can be checked on time.
Article
Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.
Conference Paper
This article presents a new method for abnormal events detection from video sequences by combing the low-rank approximation and sparse combination learning. Motivated by the structured sparsity of video data, the low-rank approximation is introduced to capture a set of normal dictionaries. With the captured dictionaries, the sparse combination learning is utilized to fit training samples and measure the abnormality of testing samples. Multi-scale 3D gradient features, which encode the spatiotemporal information, are adopted to detect abnormal events. The benefits of the proposed method are three-fold: firstly, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; Secondly, the sparsity based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for interpreting the corresponding dynamic scene semantics; Thirdly, the proposed method is efficient and real time detection can be accomplished. Experimental results on public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.
Conference Paper
Anomaly detection, which aims to discover anomalous events, defined as having a low likelihood of occurrence, from surveillance videos, has attracted increasing interest and is still a challenge in computer vision community. In this paper, we propose an efficient anomaly detection approach which can perform both real-time and multi-scale detection. Our approach can handle the change of background. Specifically, Local Coordinate Factorization is utilized to tell whether a spatio-temporal video volume (STV) belongs to an anomaly, which can effectively detect spatial, temporal and spatio-temporal anomalies. And we employ Spatio-temporal Pyramid (STP) to capture the spatial and temporal continuity of an anomalous event, enabling our approach to handle multi-scale and complicated events. We also propose an online method to update the local coordinates, which makes our approach self-adaptive to background change which typically occurs in real-world setting. We conduct extensive experiments on several publicly available datasets for anomaly detection, and the results show that our approach can outperform state-of-the-art approaches, which verifies the effectiveness of our approach.
Chapter
In this paper, a novel approach for automatic anomaly detection in surveillance video is proposed. It is highly efficient for real-time detection. It can also handle multi-scale detection and can cope with both spatial and temporal anomalies. Specifically, features capturing both appearance and motion characteristic are extracted from densely sampled spatio-temporal video volume (STV). And to bridge the semantic gap between low-level visual feature and high-level event, we use the middle-level visual attributes as the intermediary. These three-level framework is modeled as an Extreme Learning Machine (ELM) which can effectively and efficiently tell whether a STV belongs to an anomalous event. We also use the Spatio-temporal Pyramid (STP) to capture the spatial and temporal continuity of an anomalous event , enabling our approach to cope with multi-scale and complicated events. Experiments on several datasets are carried out and the superior performance compared to state-of-the-art approaches verifies the effectiveness of our approach.
Article
This paper presents a content-adaptively sparse reconstruction method for abnormal events detection by exploiting the low-rank property of video sequences. In dictionary learning phase, the bases which describe more important characteristics of the normal behavior patterns are assigned with lower reconstruction costs. Based on the low-rank property of the bases captured by the low-rank approximation, a weighted sparse reconstruction method is proposed to measure the abnormality of testing samples. Multiscale 3-D gradient features, which encode the spatiotemporal information, are adopted as the low level descriptors. The benefits of the proposed method are threefold: first, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; second, the sparsity-based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for representing the dynamic scene semantics; and third, based on the weighted sparse reconstruction method, the proposed method is more efficient for detecting the abnormal events. Experimental results on the public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.
Conference Paper
Safety is considered as one of the most crucial aspects in the modern transportation domain. In this paper, we benefit from the videos captured by multiple external video sensors from infrastructure, and propose an algorithm to perceive the environment via these data from different aspects. The algorithm consists of two parts: the descriptor for representing the event and the classification method for analyzing the scenes. The covariance matrix feature descriptor is proposed to fuse the optical flow and the intensity of the image, and the nonlinear one-class SVM with a multi-kernel strategy is used to detect the unusual events in the scene. The method is applied to analyze events in the video surveillance dataset with promising results obtained.
Chapter
In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocity as well as regional observations unlike the existing approaches. In addition, the proposed framework combined with Hidden Markov Model can cover the temporal transition of the scene state, which is useful in checking violation of the rule that some conflict topics (e.g., two cross traffic patterns) should not occur at the same time. To achieve online learning even with the complexity of the proposed model, we suggest a novel learning scheme instead of collapsed Gibbs sampling. The proposed two-stage greedy learning scheme is not only efficient at reducing the search space but also accurate in a way that the accuracy of online learning becomes not worse than that of the batch learning. To validate the performance of our method, experiments were conducted on various datasets. Experimental results show that our model explains satisfactorily the trajectory patterns with respect to scene understanding, anomaly detection, and prediction.
Article
Video, rich in visual real-time content, is however, difficult to interpret and analyse. Video collections necessarily have large data volume. Video analytics strives to automatically discover patterns and correlations present in the large volume of video data, which can help the end-user to take informed and intelligent decisions as well as predict the future based on the patterns discovered across space and time. In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
Article
In this paper, we propose an algorithm to detect abnormal events based on video streams. The algorithm is based on optical flow descriptor and one-class SVM classifier. The optical flow is computed at each pixel of the video frame, and the one-class SVM, after a learning period characterizing normal behavior, detects the abnormal blobs in the current frame. As the algorithm is not based on object tracking, it can work in crowded scenes where the tracking-based methods might fail. Extensive testing on benchmark datasets corroborates the effectiveness of the proposed detection method.
Article
In order to meet the needs of intelligent video surveillance, an unsupervised abnormal detecting algorithm was proposed. Firstly, model of mixture of Gaussians was used to extract the motion area, and the motion area was labeled. Then, observation sequence updated in real-time of feature matrix was established by the optical flow features obtained from labeled area which was normalized to the feature matrix. Finally, applying reconstruction works of two-dimensional principal component analysis on the sequence, abnormal behavior can be detected according to the energy ratio between the recovered feature matrix and original feature matrix. Experiments were conducted on various video datasets, which shows the effectiveness of the proposed method.
Conference Paper
This paper proposes a new locally adaptive boundary evolution algorithm for level set methods (LSM)-based novelty detection. The proposed approach consists of level set function construction, boundary evolution, and evolution termination. It utilises the exterior data points lying outside the decision boundary to effect the segments of the boundary that need to be locally evolved in order to make the boundary better fit the data distribution, so it can evolve boundary locally without requiring knowing explicitly the decision boundary. The experimental results demonstrate that the proposed approach can effectively detect novel events as compared to the reported LSM-based novelty detection method with global boundary evolution scheme and four representative novelty detection methods when there is an exacting error requirement on normal events.
Article
Full-text available
We present a comparative study of several state-of-the-art background subtraction methods. Approaches ranging from simple background subtraction with global thresholding to more sophisticated statistical methods have been implemented and tested on different videos with ground truth. The goal is to provide a solid analytic ground to underscore the strengths and weaknesses of the most widely implemented motion detection methods. The methods are compared based on their robustness to different types of video, their memory requirements, and the computational effort they require. The impact of a Markovian prior as well as some postprocessing operators are also evaluated. Most of the videos used come from state-of-the-art benchmark databases and represent different challenges such as poor SNR, multimodal background motion, and camera jitter. Overall, we not only help to better understand for which type of videos each method best suits but also estimate how, sophisticated methods are better compared to basic background subtraction methods.
Article
Full-text available
We describe a single-camera system capable of detecting abandoned packages under severe occlusions, which leads to complications on several levels. The first arises when frames containing only background pixels are unavailable for initializing the background model - a problem for which we apply a novel discriminative measure. The proposed measure is essentially the probability of observing a par- ticular pixel value, conditioned on the probability that no motion is detected, with the pdf on which the latter is based being estimated as a zero-mean and unimodal Gaussian dis- tribution from observing the difference values between suc - cessive frames. We will show that such a measure is a pow- erful discriminant even under severe occlusions, and can deal robustly with the foreground aperture effect - a proble m inherently caused by differencing successive frames. The detection of abandoned packages then follows at both the pixel and region level. At the pixel-level, an "abandoned pixel" is detected as a foreground pixel, at which no mo- tion is observed. At the region-level, abandoned pixels are ascertained in a Markov Random Field (MRF), after which they are clustered. These clusters are only finally classi- fied as abandoned packages, if they display temporal per- sistency in their size, shape, position and color propertie s, which is determined using conditional probabilities of the se attributes. The algorithm is also carefully designed to avo id any thresholding, which is the pitfall of many vision sys- tems, and which significantly improves the robustness of our system. Experimental results from real-life train stationse- quences demonstrate the robustness and applicability of ou r algorithm. ages in places with high human densities introduced several challenging problems caused by severe occlusions, includ- ing the difficulties faced in modeling the background and identifying static objects, and if only a single camera is co n- sidered, only limited glimpses of the abandoned package are available over a short period of time. We consider only a single camera in this paper, with the motivation that a robust single-camera algorithm operating under severe occlusions, when extended to multiple cameras, will be extremely use- ful. Given a single camera, a typical approach to the prob- lem would be to perform change detection, followed by a threshold-based approach to detect static objects, befor e classifying them as possible packages based on appearance. Several researchers have thus focused on first building a background model, with the assumption that frames con- taining only background pixels are available (e.g., ( 3, 21))
Article
Full-text available
We explore a location based approach for behavior modeling and abnormality detection. In contrast to the conventional object based approach where an object may first be tagged, identified, classified, and tracked, we proceed directly with event characterization and behavior modeling at the pixel(s) level based on motion labels obtained from background subtraction. Since events are temporally and spatially dependent, this calls for techniques that account for statistics of spatiotemporal events. Based on motion labels, we learn co-occurrence statistics for normal events across space-time. For one (or many) key pixel(s), we estimate a co-occurrence matrix that accounts for any two active labels which co-occur simultaneously within the same spatiotemporal volume. This co-occurrence matrix is then used as a potential function in a Markov random field (MRF) model to describe the probability of observations within the same spatiotemporal volume. The MRF distribution implicitly accounts for speed, direction, as well as the average size of the objects passing in front of each key pixel. Furthermore, when the spatiotemporal volume is large enough, the co-occurrence distribution contains the average normal path followed by moving objects. The learned normal co-occurrence distribution can be used for abnormal detection. Our method has been tested on various outdoor videos representing various challenges.
Conference Paper
Full-text available
This paper proposes a novel method for detecting nonconforming trajectories of objects as they pass through a scene. Existing methods mostly use spatial features to solve this problem. Using only spatial information is not adequate; we need to take into consideration velocity and curvature information of a trajectory along with the spatial information for an elegant solution. Our method has the ability to distinguish between objects traversing spatially dissimilar paths, or objects traversing spatially proximal paths but having different spatio-temporal characteristics. The method consists of a path building training phase and a testing phase. During the training phase, we use graph-cuts for clustering the trajectories, where the Hausdorff distance metric is used to calculate the edge weights. Each cluster represents a path. An envelope boundary and an average trajectory are computed for each path. During the testing phase we use three features for trajectory matching in a hierarchical fashion. The first feature measures the spatial similarity while the second feature compares the velocity characteristics of trajectories. Finally, the curvature features capture discontinuities in velocity, acceleration, and position of the trajectory. We use real-world pedestrian sequences to demonstrate the practicality of our method.
Article
Full-text available
We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple "atomic" activities, and interactions. Atomic activities are modeled as distributions over low-level visual features, and multi-agent interactions are modeled as distributions over atomic activities. These models are learnt in an unsupervised way. Given a long video sequence, moving pixels are clustered into different atomic activities and short video clips are clustered into different interactions. In this paper, we propose three hierarchical Bayesian models, Latent Dirichlet Allocation (LDA) mixture model, Hierarchical Dirichlet Process (HDP) mixture model, and Dual Hierarchical Dirichlet Processes (Dual-HDP) model. They advance existing language models, such as LDA [1] and HDP [2]. Our data sets are challenging video sequences from crowded traffic scenes and train station scenes with many kinds of activities co-occurring. Without tracking and human labeling effort, our framework completes many challenging visual surveillance tasks of board interest such as: (1) discovering typical atomic activities and interactions; (2) segmenting long video sequences into different interactions; (3) segmenting motions into different activities; (4) detecting abnormality; and (5) supporting high-level queries on activities and interactions.
Article
Full-text available
We propose a novel method to model and learn the scene activity, observed by a static camera. The proposed model is very general and can be applied for solution of a variety of problems. The motion patterns of objects in the scene are modeled in the form of a multivariate nonparametric probability density function of spatiotemporal variables (object locations and transition times between them). Kernel Density Estimation is used to learn this model in a completely unsupervised fashion. Learning is accomplished by observing the trajectories of objects by a static camera over extended periods of time. It encodes the probabilistic nature of the behavior of moving objects in the scene and is useful for activity analysis applications, such as persistent tracking and anomalous motion detection. In addition, the model also captures salient scene features, such as the areas of occlusion and most likely paths. Once the model is learned, we use a unified Markov Chain Monte Carlo (MCMC)-based framework for generating the most likely paths in the scene, improving foreground detection, persistent labeling of objects during tracking, and deciding whether a given trajectory represents an anomaly to the observed motion patterns. Experiments with real-world videos are reported which validate the proposed approach.
Conference Paper
Full-text available
The detection of events that differ from what is considered normal is, arguably, the most important task for camera-based surveillance. Clearly, the definition of normal behavior differs from one application to another, and, therefore, approaches to its detection differ as well. In the case of intrusion monitoring, simple motion detection may be sufficient, such as based on background luminance/color modeling. However, in more complex scenarios, such as the detection of abandoned luggage, more advanced approaches have been developed, often relying on object path modeling. In this paper, we describe a new model for representing normality. Our model, that we call a behavior image, is low-dimensional and based on dynamics of luminance/color profiles, however it does not require explicit estimation of object paths. The process of estimating visual abnormality is then a simple comparison of training and observed behavior images, that we call behavior subtraction. We describe a new practical implementation of our model that is based on average activity. It is easy to program and requires little processing power and memory. Moreover, it is robust to motion detection errors, such as those resulting from parasitic background motion (e.g., heavy rain/snow, camera jitter). Most importantly, however, the method is not content-specific, and, therefore, is applicable to the monitoring of humans, cars or other objects in both uncluttered and highly-cluttered scenes. We support these claims by including various experimental results, from urban traffic, through sport scenes to natural environment.
Conference Paper
Full-text available
We explore a location-basedapproachforbehaviormod- eling and abnormality detection. In contrast to the con- ventional object-based approach where an object may first be tagged, identified, classified, and tracked, we proceed directly with event characterization and behavior model- ing at the pixel(s) level based on motion labels obtained from background subtraction. Since events are temporally and spatially dependent, this calls for techniques that ac- count for statistics of spatio-temporal events. Based on mo- tion labels, we learn co-occurrence statistics for normal events across space-time. For one (or many) key pixel(s), we estimate a co-occurrence matrix that accounts for any two active labels which co-occur simultaneously within the same spatio-temporal volume. This co-occurrence matrix is then used as a potential function in a Markov Random Field (MRF) model to describe the probability of observa- tions within the same spatio-temporal volume. The MRF distribution implicitly accounts for speed, direction, as well as the average size of the objects passing in front of each key pixel. Furthermore, when the spatio-temporal volume is large enough, the co-occurrence distribution contains the average normal path followed by moving objects. The learned normal co-occurrence distribution can be used for abnormaldetection. Our method hasbeen tested on various outdoor videos representing various challenges.
Article
Full-text available
In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz's Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. In particular, we developed a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DML-HMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and outdoor scenes. Our experimental results demonstrate that the performance of a DML-HMM on modelling group activities in a noisy and cluttered scene is superior compared to those of other comparable dynamic probabilistic networks including a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM).
Article
Full-text available
As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. This is evidenced by the emergence of face recognition conferences such as AFGR [1] and AVBPA [2], and systematic empirical evaluations of face recognition techniques, including the FERET [3, 4, 5, 6] and XM2VTS [7] protocols. There are at least two reasons for this trend; the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. This paper provides an up-to-date critical survey of still- and video-based face recognition research. 1 The support of the Office of Naval Research under Grants N00014-95-1-0521 and N00014-00-1-0908 is gratefully acknowledged. 2 Vision Technologies Lab, Sarnoff Corporation, Princeton, NJ 08543-5300. 3 Center for Automation Research, University of Maryland, College Park...
Article
Full-text available
In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection.
Article
Full-text available
Visual surveillance is an important computer vision research problem. As more and more surveillance cameras appear around us, the demand for automatic methods for video analysis is increasing. Such methods have broad applications including surveillance for safety in public transportation, public areas, and in schools and hospitals. Automatic surveillance is also essential in the fight against terrorism. In this light, the PETS 2006 data corpus contains seven left-luggage scenarios with increasing scene complexity. The challenge is to automatically determine when pieces of luggage have been abandoned by their owners using video data, and set an alarm. In this paper, we present a solution to this problem using a two-tiered approach. The first step is to track objects in the scene using a trans-dimensional Markov Chain Monte Carlo tracking model suited for use in generic blob tracking tasks. The tracker uses a single camera view, and it does not differentiate between people and luggage. The problem of determining if a luggage item is left unattended is solved by analyzing the output of the tracking system in a detection process. Our model was evaluated over the entire data set, and successfully detected the left-luggage in all but one of the seven scenarios.
Article
Full-text available
We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions, which are intersections of paths commonly taken by objects, related to activities in the scene are also modeled. Dual-HDP advances the existing Hierarchical Dirichlet Processes (HDP) language model. HDP only clusters co-occurring words from documents into topics and automatically decides the number of topics. Dual-HDP co-clusters both words and documents. It learns both the numbers of word topics and document clusters from data. Under our problem settings, HDP only clusters observations of objects, while Dual-HDP clusters both observations and trajectories. Experiments are evaluated on two data sets, radar tracks collected from a maritime port and visual tracks collected from a parking lot.
Article
Full-text available
The amount of captured video is growing with the increased numbers of video cameras, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, most captured video is never watched or examined. Video synopsis is an effective tool for browsing and indexing of such a video. It provides a short video representation, while preserving the essential activities of the original video. The activity in the video is condensed into a shorter period by simultaneously showing multiple activities, even when they originally occurred at different times. The synopsis video is also an index into the original video by pointing to the original time of each activity. Video Synopsis can be applied to create a synopsis of an endless video streams, as generated by webcams and by surveillance cameras. It can address queries like "Show in one minute the synopsis of this camera broadcast during the past day''. This process includes two major phases: (i) An online conversion of the endless video stream into a database of objects and activities (rather than frames). (ii) A response phase, generating the video synopsis as a response to the user's query.
Article
Full-text available
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.
Article
Full-text available
We address the problem of unusual-event detection in a video sequence. Invariant subspace analysis (ISA) is used to extract features from the video, and the time-evolving properties of these features are modeled via an infinite hidden Markov model (iHMM), which is trained using "normal"/"typical" video. The iHMM retains a full posterior density function on all model parameters, including the number of underlying HMM states. Anomalies (unusual events) are detected subsequently if a low likelihood is observed when associated sequential features are submitted to the trained iHMM. A hierarchical Dirichlet process framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via Markov chain Monte Carlo and using a variational Bayes formulation. Comparisons are made to modeling based on conventional maximum-likelihood-based HMMs, as well as to Dirichlet-process-based Gaussian-mixture models.
Article
Full-text available
A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms
Article
Full-text available
Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive background subtraction method that models each pixel as a mixture of Gaussians and uses an online approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co-occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site
Article
A video sequence is a much richer source of visual information than a still image. This is primarily because of the capture of motion. Although a single image provides a snapshot of the scene, a sequence of images registers the dynamics in it. The discussion of motion in this chapter is carried out from the point of view of video processing and compression. A classification is made based on models, estimation criteria, and the search strategies used. Motion detection is formulated as hypothesis testing, maximum a posteriori probability (MAP) estimation, and a variational problem. Motion estimation is described in two parts. First models, estimation criteria, and search strategies are discussed. Then five motion estimation algorithms are described, of which three are based on the models supported by the current video compression standards. Both motion detection and estimation are illustrated by experimental results.
Article
"Background subtraction" is an old technique for finding moving objects in a video sequence for example, cars driving on a freeway. The idea is that subtracting the current image from a timeaveraged background image will leave only nonstationary objects. It is, however, a crude approximation to the task of classifying each pixel of the current image; it fails with slow-moving objects and does not distinguish shadows from moving objects. The basic idea of this paper is that we can classify each pixel using a model of how that pixel looks when it is part of different classes. We learn a mixture-of-Gaussians classification model for each pixel using an unsupervised technique- an efficient, incremental version of EM. Unlike the standard image-averaging approach, this automatically updates the mixture component for each class according to likelihood of membership; hence slow-moving objects are handled perfectly. Our approach also identifies and eliminates shadows much more effectively than other techniques such as thresholding. Application of this method as part of the Roadwatch traffic surveillance project is expected to result in significant improvements in vehicle identification and tracking.
Article
Bahadur-Kiefer approximations for generalized quantile processes as defined in Einmahl and Mason (1992) are given which generalize results for the classical one-dimensional quantile processes. An as application we consider the special case of the volume process of minimum volume sets in classes of subsets of the d-dimensional Euclidean space. Minimum volume sets can be used as estimators of level sets of a density and might be useful in cluster analysis. The volume of minimum volume sets itself can be used for robust estimation of scale. Consistency results and rates of convergence for minimum volume sets are given. Rates of convergence of minimum volume sets can be used to obtain Bahadur-Kiefer approximations for the corresponding volume process and vice versa. A generalization of the minimum volume approach to non-i.i.d. problems like regression and spectral analysis of time series is discussed.
Article
We are entering an era of more intelligent cognitive vision systems. Such systems can analyse activity in dynamic scenes to compute conceptual descriptions from motion trajectories of moving people and the objects they interact with. Here we review progress in the development of flexible, generative models that can explain visual input as a combination of hidden variables and can adapt to new types of input. Such models are particularly appropriate for the tasks posed by cognitive vision as they incorporate learning as well as having sufficient structure to represent a general class of problems. In addition, generative models explain all aspects of the input rather than attempting to ignore irrelevant sources of variation as in exemplar-based learning. Applications of these models in visual interaction for education, smart rooms and cars, as well as surveillance systems is also briefly reviewed.
Article
In this paper, we present a method for human action recognition from multi-view image sequences that uses the combined motion and shape flow information with variability consideration. A combined local–global (CLG) optic flow is used to extract motion flow feature and invariant moments with flow deviations are used to extract the global shape flow feature from the image sequences. In our approach, human action is represented as a set of multidimensional CLG optic flow and shape flow feature vectors in the spatial–temporal action boundary. Actions are modeled by using a set of multidimensional HMMs for multiple views using the combined features, which enforce robust view-invariant operation. We recognize different human actions in daily life successfully in the indoor and outdoor environment using the maximum likelihood estimation approach. The results suggest robustness of the proposed method with respect to multiple views action recognition, scale and phase variations, and invariant analysis of silhouettes.
Article
We address the problem of detecting irregularities in vi- sual data, e.g., detecting suspicious behaviors in video se- quences, or identifying salient patterns in images. The term "irregular" depends on the context in which the "regular" or "valid" are defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. We pose the problem of determining the validity of visual data as a process of constructing a puz- zle: We try to compose a new observed image region or a new video segment ("the query") using chunks of data ("pieces of puzzle") extracted from previous visual exam- ples ("the database"). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas re- gions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in images and video, and for suspicious behavior recognition.
Article
A nonlinear face recognition technique based on neighborhood preserving discriminant analysis (NPDA) is proposed. The kernel trick is adopted to allow the efficient computation of local Fisher discriminant in high-dimensional feature space. Moreover, a direct solution for obtaining the optimal feature vectors in feature space is presented which can preserve the most discriminative information. The proposed algorithm is evaluated on the UMIST database, the ORL database and the FERET database by using six different methods. Experiments show that consistent and promising results are obtained.
Article
We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are first tracked in each camera view independently, and the positions and velocities of objects along trajectories are computed as features. Under a probabilistic model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. Then, it accomplishes the following tasks: 1) grouping trajectories, which belong to the same activity but may be in different camera views, into one cluster; 2) modeling paths commonly taken by objects across multiple camera views; and 3) detecting abnormal activities. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and that learning is unsupervised. Even though correspondence is not a prerequisite, after the models of activities have been learned, they can help to solve the correspondence problem, since if two trajectories in different camera views belong to the same activity, they are likely to correspond to the same object. Our approach is evaluated on a simulated data set and two very large real data sets, which have 22,951 and 14,985 trajectories, respectively.
Conference Paper
This paper presents the results of ETISEO, a performance evaluation project for video surveillance systems. Many other projects have already evaluated the performance of video surveillance systems, but more on an end-user point of view. ETISEO aims at studying the dependency between algorithms and the video characteristics. Firstly we describe ETISEO methodology which consists in addressing each video processing problem separately. Secondly, we present the main evaluation metrics of ETISEO as well as their benefits, limitations and conditions of use. Finally, we discuss about the contributions of ETISEO to the evaluation community.
Article
Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.
Article
W4 is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W4 employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them. W4 can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames. W4 can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320×240 resolution images on a 400 MHz dual-Pentium II PC
Non-chronological video synopsis and indexing Infinite Hidden Markov Models for unusual-event detection in video
  • Y Pritch
  • Rav
  • A Acha
  • S Peleg
Stochastic Processes and Applications 69 (1), 1–24. Pritch, Y., Rav-Acha, A., Peleg, S., 2008. Non-chronological video synopsis and indexing. Transactions on Pattern Analysis and Machine Intelligence 30 (11), 1971–1984. Pruteanu-Malinici, I., Carin, L., 2008. Infinite Hidden Markov Models for unusual-event detection in video. Transactions in Image Processing 17 (5), 811– 822
Face recognition: A literature survey on PatternRecognition,716– Evaluation ofTracking and Y
  • W Zhao
  • R Chellappa
  • P Phillips
  • A Rosenfeld
Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A., 2003. Face recognition: A literature survey. ACM Computing Surveys 35 (4), 399–458. on PatternRecognition,716– Evaluation ofTracking and Y. Benezeth et al./Pattern Recognition Letters 32 (2011) 423–431 431
An overview of the pets 2006 dataset
  • D Thirde
  • L Ferryman
  • J Li
Thirde, D., Ferryman, L. Li. J., 2006. An overview of the pets 2006 dataset. In: International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 47-50.
Detecting abandoned luggage items in a public space
  • K Smith
  • P Quelhas
  • D Gatica-Perez
Smith, K., Quelhas, P., Gatica-Perez, D., 2006. Detecting abandoned luggage items in a public space. Performance Evaluation of Tracking and Surveillance Workshop (PETS), 75-82.
An overview of the pets 2006 dataset
  • Thirde
A one-threshold algorithm for detecting abandoned packages under severe occlusions using a single camera
  • S.-N Lim
  • H Fujiyoshi
  • R Patil
Lim, S.-N., Fujiyoshi, H., Patil, R., 2006. A one-threshold algorithm for detecting abandoned packages under severe occlusions using a single camera. Technical Report CS-TR-4784, University of Maryland.