Nasreddine TALEB

Work place: Department of Electronics, Djillali Liabes University, Sidi Bel-Abbes, Algeria



Research Interests: Medical Image Computing, Image Processing, Image Manipulation, Image Compression, Pattern Recognition, Medical Informatics


Nasreddine TALEB received the M.Sc. degree in computer engineering from Boston University, Boston, MA, USA, the Electrical Engineering degree from Northeastern University, Boston, MA, USA, and the Ph.D. degree in electrical engineering from Djillali Liabes University, Sidi Bel-Abbes, Algeria. He is currently a Professor with the Department of Electronic Engineering, University of Djillali Liabes, where he has been teaching since 1990 and where he is also a Senior Research Scientist and the Director of the ―Communication Networks, rchitecture, and Multimedia‖ Laboratory. His principle research interests are in the fields of digital signal and image processing, image analysis, medical and satellite image applications, pattern recognition, and advanced architectures for the implementation of DSP/DIP applications

Author Articles
A Novel Object Position Coding for Multi-Object Tracking using Sparse Representation


DOI:, Pub. Date: 8 Jul. 2015

Multi-object tracking is a challenging task, especially when the persistence of the identity of objects is required. In this paper, we propose an approach based on the detection and the recognition. To detect the moving objects, a background subtraction is employed. To solve the recognition problem, a classification system based on sparse representation is used. With an online dictionary learning, each detected object is classified according to the obtained sparse solution. Each column of the used dictionary contains a descriptor representing an object. Our main contribution is the representation of the moving object with a descriptor derived from a novel representation of its 2-D position and a histogram-based feature, improved by using the silhouette of this object. Experimental results show that the approach proposed for describing moving objects, combined with the classification system based on sparse representation provides a robust multi-object tracker in videos involving occlusions and illumination changes.

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