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ABSTRACT: Vision-based systems for traffic surveillance have an impressive spread both for their practical application and interest as research issue. The most common approach used for vision-based traffic surveillance consists of a fast segmentation of Moving Visual Objects (MVOs) in the scene together with an intelligent reasoning module capable of identifying, tracking and classifying the MVOs in dependency of the system goal. In this paper we describe our approach for MVOs segmentation in an unstructured traffic environment. We consider complex situations with moving people, vehicles, infrastructures that have different aspect model and motion model. In this case we define a approach based on background subtraction statistic and knowledge-based background We show many results of real-time tracking MVOs in outdoor traffic scene such parking area, intersections, entrance with barriers.
02/2013;
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ABSTRACT: On-line action recognition from a continuous stream of actions is still an open problem with fewer solutions proposed compared to time-segmented action recognition. The most challenging task is to classify the current action while finding its time boundaries at the same time. In this paper we propose an approach capable of performing on-line action segmentation and recognition by means of batteries of HMM taking into account all the possible time boundaries and action classes. A suitable Bayesian normalization is applied to make observation sequences of different length comparable and computational optimizations are introduce to achieve real-time performances. Results on a well known action dataset prove the efficacy of the proposed method.
Image Processing (ICIP), 2009 16th IEEE International Conference on; 12/2009
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ABSTRACT: The paper describes the use of data analysis techniques in the computer-vision inspection of industrial workpieces. Computer-vision
inspection aims at accomplishing quality verification of fabricated parts by means of automated visual procedures. Gathering
the visual information into models proves a critical task, especially when subjective judgement is involved in quality verification.
In this work, intelligent data analysis techniques based on symbolic learning by examples have been explored in order to automatically
devise and parametrize effective quantitative models. The paper reports and discusses the experimental results achieved in
an industrial application.
06/2006: pages 223-234;
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ABSTRACT: Cache performance is strongly influenced by the type of locality embodied in programs. In particular, multimedia programs handling images and videos are characterized by a bidimensional spatial locality, which is not adequately exploited by standard caches. In this paper we propose novel cache prefetching techniques for image data, called neighbor prefetching, able to improve exploitation of bidimensional spatial locality. A performance comparison is provided against other assessed prefetching techniques on a multimedia workload (with MPEG-2 and MPEG-4 decoding, image processing, and visual object segmentation), including a detailed evaluation of both the miss rate and the memory access time. Results prove that neighbor prefetching achieves a significant reduction in the time due to delayed memory cycles (more than 97% on MPEG-4 with respect to 75% of the second performing technique). This reduction leads to a substantial speedup on the overall memory access time (up to 140% for MPEG-4). Performance has been measured with the PRIMA trace-driven simulator, specifically devised to support cache prefetching.
IEEE Transactions on Multimedia 09/2004; · 1.93 Impact Factor
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ABSTRACT: Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.
IEEE Transactions on Pattern Analysis and Machine Intelligence 11/2003; 25(10):1337- 1342. · 4.91 Impact Factor
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ABSTRACT: Data cache prefetching is an effective technique to improve performance of cache memories, whenever the prefetching algorithm is able to correctly predict useful data to be prefetched. To this aim, adequate information on the programs data locality must be used by the prefetching algorithm. In. particular, multimedia applications are characterized by a substantial amount of image and video processing, which exhibits spatial locality in both the dimensions of the 2D data structures used for images and frames. However, in multimedia programs many memory references are made also to non-image. data, characterized by standard spatial locality. In this work, we explore the adoption of different prefetching techniques in dependence of the data type (i. e., image and non-image), thus making it possible to tune the prefetching algorithms to the different forms of locality, and achieving overall performance optimization. In order to prevent. interference between. the two different data types, a split cache with two separated caches for image and non-image data is also evaluated as an alternative to a standard unified cache. Results on a multimedia workload (MPEG-2 and MPEG-4 decoders) show that standard prefetching techniques such as One-block-lookahead and the Stride Prediction Table are effective for standard data, while novel 2D prefetching techniques perform best on image data. hi addition, at a parity of size, unified caches offer in general better performance that split caches, thank to the more flexible allocation of a unified cache space
Performance, Computing, and Communications Conference, 2002. 21st IEEE International; 02/2002
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ABSTRACT: A performance analysis of MPEG-4 encoder and decoder programs on a standard personal computer is presented. The paper first describes the MPEG-4 computational load and discusses related works, then outlines the performance analysis. Experimental results show that while the decoder program can be easily executed in real time, the encoder requires execution times in the order of seconds per frame which call for substantial optimisation to satisfy real-time constraints.
Video/Image Processing and Multimedia Communications 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom; 02/2002
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ABSTRACT: Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a general-purpose method for segmentation of moving visual objects (MVO) based on an object-level classification in MVO, ghosts and shadows. Background suppression needs the background model to be estimated and updated: we use motion and shadow information to selectively exclude from the background model MVO and their shadows, while retaining ghosts. The color information (in the HSV color space) is exploited to shadow suppression and, consequently, to enhance both MVO segmentation and background update
Image Analysis and Processing, 2001. Proceedings. 11th International Conference on; 10/2001
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ABSTRACT: Prefetching is a widely adopted technique for improving
performance of cache memories. Performances are typically affected by
the design parameters, such as cache size and associativity, but also by
the type of locality embodied in the programs. In particular multimedia
tools and programs handling images and video are characterized by a
bi-dimensional spatial locality that could be greatly exploited by the
inclusion of prefetching in the cache architecture. In this paper we
compare some prefetching techniques for multimedia programs (such as
MPEG compression, image processing, visual object segmentation) by
performing a detailed evaluation of the memory access time. The goal is
to prove that a significant speedup can be achieved by using either
standard prefetching techniques (such as OBL or adaptive prefetching) or
some innovative and image-oriented prefetching methods like the neighbor
prefetching described in the paper. Performance are measured with the
PRIMA trace-driven simulator
Performance, Computing, and Communications, 2001. IEEE International Conference on.; 05/2001
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ABSTRACT: Video-surveillance and traffic analysis systems can be heavily
improved using vision-based techniques able to extract, manage and track
objects in the scene. However, problems arise due to shadows. In
particular, moving shadows can affect the correct localization,
measurements and detection of moving objects. This work aims to present
a technique for shadow detection and suppression used in a system for
moving visual object detection and tracking. The major novelty of the
shadow detection technique is the analysis carried out in the HSV color
space to improve the accuracy in detecting shadows. Signal processing
and optic motivations of the approach proposed are described. The
integration and exploitation of the shadow detection module into the
system are outlined and experimental results are shown and evaluated
Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE; 02/2001
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ABSTRACT: The paper presents an approach for detecting vehicles in urban
traffic scenes by means of rule-based reasoning on visual data. The
strength of the approach is its formal separation between the low-level
image processing modules and the high-level module, which provides a
general-purpose knowledge-based framework for tracking vehicles in the
scene. The image-processing modules extract visual data from the scene
by spatio-temporal analysis during daytime, and by morphological
analysis of headlights at night. The high-level module is designed as a
forward chaining production rule system, working on symbolic data, i.e.,
vehicles and their attributes (area, pattern, direction, and others) and
exploiting a set of heuristic rules tuned to urban traffic conditions.
The synergy between the artificial intelligence techniques of the
high-level and the low-level image analysis techniques provides the
system with flexibility and robustness
IEEE Transactions on Intelligent Transportation Systems 07/2000; · 3.45 Impact Factor
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ABSTRACT: Effective detection of vehicles in urban traffic scenes can be achieved by exploiting image analysis techniques. Nevertheless, vehicle detection in daytime and at night can't be approached with the same image analysis algorithms, due to the strongly different illumination conditions. This paper describes the two different sets of image analysis algorithms that have been used in the VTTS system (Vehicular Traffic Tracking System) for extracting vehicles from image sequences acquired in daytime and at night. In the system, a supervising level selects the set of algorithms to apply and performs vehicle tracking under control of a rule-based decision module. The paper describes the tracking module, and reports experimental results for both vehicle detection and tracking. I. INTRODUCTION Automatic vehicle detection in traffic scenes is an important goal in the field of transportation systems, since it allows the enforcement of traffic policies with precise information on traffic. Image an...
03/2000;
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ABSTRACT: The workload of multimedia applications has a strong impact on cache memory performance, since the locality of memory references embedded in multimedia programs differs from that of traditional programs. In many cases, standard cache memory organization achieves poorer performance when used for multimedia. A widely explored approach to improve cache performance is hardware prefetching that allows the pre-loading of data in the cache before they are referenced. However, existing hardware prefetching approaches partially miss the potential performance improvement, since they are not tailored to multimedia locality. In this paper we propose novel effective approaches to hardware prefetching to be used in image processing programs for multimedia. Experimental results are reported for a suite of multimedia image processing programs including convolutions with kernels, MPEG-2 decoding, and edge chain coding
Computer Architectures for Machine Perception, 2000. Proceedings. Fifth IEEE International Workshop on; 02/2000
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[show abstract]
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ABSTRACT: The most common approach used for vision-based traffic
surveillance consists of a fast segmentation of moving visual objects
(MVOs) in the scene together with an intelligent reasoning module
capable of identifying, tracking and classifying the MVOs in dependency
of the system goal. In this paper we describe our approach for MVOs
segmentation in an unstructured traffic environment. We consider complex
situations with moving people, vehicles and infrastructures that have
different aspect model and motion model. In this case we define a
specific approach based on background subtraction with statistic and
knowledge-based background update. We show many results of real-time
tracking of traffic MVOs in outdoor traffic scene such as roads, parking
area intersections, and entrance with barriers
Intelligent Transportation Systems, 2000. Proceedings. 2000 IEEE; 02/2000
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ABSTRACT: The paper explores cache strategies for multimedia. Although many architectural improvements have been designed for multimedia, the cache structure and the standard caching policies of general-purpose processors exhibit poor performance in exploiting the 2D spatial locality typical of programs handling and processing images. We propose a novel caching approach, suitably tailored to the requirement of multimedia programs. Our proposal exploits hardware pre-fetching for allocating in cache, blocks of data that satisfy the 2D spatial locality requirements. Results refer to a benchmark suite of multimedia programs including MPEG decoding and image processing programs with different data dependency and access schemes to image data
Multimedia Computing and Systems, 1999. IEEE International Conference on; 08/1999
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ABSTRACT: . This paper analyzes hardware pre-fetching techniques for caching images. Performances are evaluated with respect to different classes of image processing algorithms, namely raster-scan and propagative algorithms, common in computer vision and multimedia applications. Sequential pre-fetching and adaptive pre-fetching are compared with the proposed adaptive local pre-fetching that results to be more efficient, mirroring the two-dimensional spatial locality of image processing algorithms. 1. Introduction This paper focuses on caching strategies for caching images in computer vision and multimedia application. The interest in caching images follows the emerging trend that attempts to include some functional units for handling multimedia data and in particular images also in generalpurpose processors. However, although effective research activity has addressed dedicated execution units (see the Intel MMX, HP MAX, Sun VIS pixel-oriented instruction sets), no large advances have been made...
07/1999;
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ABSTRACT: We propose a novel approach for recognizing 3D CAD-made objects in complex range images containing several overlapped and different objects. Objects are modeled by a graph whose nodes are surfaces and arcs are surface relations. We propose an object-centered graph model, called visual constraint graph (VC-graph), with special visual constraints modeling occlusions between object surfaces. The VC-graph is used for recognizing objects from each possible point of view, instead of evaluating many different single-view graphs. The reasoning engine is based on an original extension of the constraint satisfaction problem (CSP) paradigm, called interactive CSP (ICSP). CSP requires the acquisition of all surfaces before starting constraint propagation; instead, ICSP guides the acquisition of new surfaces only on-demand, without computing useless information and focusing attention only on significant image parts
Image Analysis and Processing, 1999. Proceedings. International Conference on; 02/1999
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ABSTRACT: Computer vision-based traffic flow monitoring is of major importance for enforcing traffic management policies. Information such as the number of vehicles passing on a road per time unit, or vehicles' turning rates at intersections are exploited by traffic management policies to supervise traffic-light timings. Computer vision-based traffic flow monitoring requires extraction of moving vehicles from traffic scenes in real time. To accomplish this task, efficient algorithms must be used and effective, low-cost hardware implementation must be pursued. This paper first describes the algorithms used in the VTTS (vehicular traffic tracking system) to achieve segmentation of moving vehicles. Then, hardware implementation on a re-programmable FPGA-based board is described in detail
Image Analysis and Processing, 1999. Proceedings. International Conference on; 02/1999
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ABSTRACT: Emerging trends in computer design attempt to include specific
solutions for handling images also in general-purpose computers, because
of the current spread of multimedia, image processing and computer
graphics applications. In this context, we propose hardware pre-fetching
techniques specific for caching images: the main issue we state is that
most algorithms working on images exhibit a 2D spatial locality that is
not taken into account in current cache organization and data access
strategies. To this aim we propose an adaptive local pre-fetching for
the image data type; this technique, mirroring the two-dimensional
spatial locality of image processing algorithms, results in being more
efficient than other approaches, such as sequential pre-fetching and
adaptive pre-fetching. Performance is evaluated on different classes of
image processing algorithms, namely raster-scan and propagative
algorithms, common in computer vision and multimedia applications
High Performance Computing, 1998. HIPC '98. 5th International Conference On; 01/1999
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ABSTRACT: . This paper addresses an automatic knowledge discovery process from a database of images in a context of Automated Visual Inspection (AVI). AVI is the field of computer vision addressing quality inspection of industrial products, even under informal quality models. When modelling informal knowledge, one of the most critical point turns out to be the correct and efficient translation of human experience into a set of rules. The paper focuses on the use of machine learning in inspection of industrial workpieces. It shows how machine learning can be exploited for data mining purposes and more specifically for selecting a minimal set of visual primitives, in order to perform reliable and robust classification of the inspected components. Eventually, the industrial application and the inspection system are presented in details. 1. Introduction Data mining relies on extracting meaningful, previously unknown information from large databases, which could be highly profitable for business app...
11/1997;
