Walid Ayedi

• Doctor of Philosophy in Computer Science
• Professor (Associate) at University of Sfax

Video surveillance has been a major area of focus for researchers and engineers. Actually, video surveillance includes several useful and complex tasks such as tracking, human detection, re-identification and recognition. Multi-scale covariance (MSCOV) descriptor has recently grown in interest due to its good performances for person detection, re-i...

In order to handle the complex databases of acquired images in the security area, a robust and adaptive framework for Video Surveillance Data Mining as well as for multi-shot pedestrian (re)-identification is required. The pedestrian’s signature must be invariant and robust against the noise and uncontrolled variation. In this paper a new fast Gait...

Wireless Sensor networks (WSNs) is an efficient and emerging area of Computer Science Engineering which has been currently employed in various fields of engineering particularly in communication system to make it effective and reliable. It is important to maintain the basic security level for different types of attacks like both external and intern...

Underwater image processing is widely increased over the last decade. It is a fundamental process for a most part of underwater research applications, because of the need of data acquisition. In this paper we will propose a novel approach of pipe detection in submarine environment. The system draws much of its power from a representation that descr...

Object detection is the fundamental process for the majority of the investigation projects in the submarine environment, and object detection is mainly based on image description done by the appropriate descriptor. In this paper we select and optimize parameters of multi-scale covariance descriptor for object detection in the submarine context. We...

Multi-shot person re-identification in non-overlapping camera networks has become an important research area. In order to tackle this problem, a robust and adaptive person modeling against occlusion and uncontrolled changes is required. In this paper, a new Multi-Scale Video Covariance (MS-VC) unsupervised approach was proposed to efficiently descr...

Person matching is an important topic in video- surveillance and can be used to design detection, tracking and recognition systems. Multi-scale covariance (MSCOV) is considered as one of the most promising descriptors for person matching. Unfortunately, implementing such descriptors for person matching requires heavy computation. For a system that...

Multi-shot person re-identification in video surveillance applications has been regarded as a very difficult problem due to the low-quality of video frames, context changes and the rich intra-personal appearance variations. In this paper, we present a novel video covariance approach called VICOVAP, which considers video sequences to extract appeara...

Multi-shot human re-identification is a major challenge because of the large variations in a human’s appearance caused by different types of noise such as occlusion, viewpoint and illumination variations. In this paper, we presented a novel Gabor-LBP based video covariance descriptor, called GL-VC descriptor, which considers image sequences to extr...

Multi-shot person re-identification is a major challenge because of the large variations in a human’s appearance caused by different types of noise such as occlusion, viewpoint and illumination variations. In this paper, we presented the accordion representation based multi-scale covariance descriptor, called AR-MSCOV descriptor, which considers in...

This paper presents a novel approach to solve the problem of person re-identification in non-overlapping camera views. We propose an appearance based method for person re-identification that condenses a set of frames of the same individual into the multi-class classifier SVM (Support Vector Machine). Still, the choice of different and most expressi...

Covariance descriptor has good performance for person detection systems. However, it has high execution time. Multiprocessors systems are usually adopted to speed up the execution of these systems. In this paper, an optimized parallel model for covariance person detection is implemented using a high-level parallelization procedure. The main charact...

Outlier detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as one-class classification, in which a model is constructed to describe normal training data. In wireless sensor networks (WSNs), the outlier detection process is a necessary step in building sensor...

Due to the complexity and the high performance requirement of person detection application, the design of embedded systems is the subject of different types of design constraints such as execution time, time to market, energy consumption, etc. Some methodologies were proposed in order to satisfy the different design constraints. This paper presents...

Outliers in wireless sensor networks are measurements that deviate from the normal model of sensed data and result from errors, events or malicious attacks on the network. The dynamic nature of sensor data and the specificity of the wireless sensor network make traditional outlier detection techniques unsuitable for direct application in such conte...

With the recent technological advances in wireless communications, integrated digital circuits; development of wireless sensor networks has been enabled and become dramatically feasible. WSNs are large networks made of a numerous number of sensor nodes with sensing, computation, and wireless communications capabilities. Many various routing, power...

This paper presents a study on human detection using the multi-scale covariance descriptor (MSCOV) proposed in a previous work [1] in which we showed the performance of this descriptor for human re-identification. In this work, we evaluate its performance in human detection. We propose a fast tree based method for multi-scale features covariance co...

With the rapid development of Wireless Sensor Networks (WSNs), the latter is increasingly getting used in critical environments. Multimedia networked applications have become more and more feasible over WSNs. The development of these types of applications requires extensive knowledge of multimedia network tools. For example, there is some research...

The proposed method consists on '3D-to-2D' transformation of the temporal frames that allows exploring the temporal redundancy of the video using 2D transforms and avoiding the computationally demanding motion compensation step. This transformation turns video spatial temporal correlation into high spatial correlation. In this paper, we explore the...

In many surveillance systems, there is a need to determine if a given object (person, group of persons, vehicle, …) has already been observed over a network of cameras. It is the object re-identification problem. Solving this problem involves matching observation of objects across disjoint camera views. Uncalibrated fixed or mobile cameras with non...

A non-predictive video coding is a new branch of emerging research area in video coding, where the motion estimation/compensation or prediction step in the temporal domain is omitted. One direction was to look for the exploitation of temporal decomposition of video frames. The proposed method consists on 3D to 2D transformation of the temporal fram...

Generally, video signal has high temporal redundancies due to the high correlation between successive frames. Actually, this redundancy has not been exploited enough by current video compression techniques. In this paper, we present a new video compression approach which tends to hard exploit the pertinent temporal redundancy in the video frames to...