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

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Author(s)

Mohamed ELBAHRI 1,* Kidiyo KPALMA 2 Nasreddine TALEB 3 Miloud CHIKR EL-MEZOUAR 3

1. Department of Computer Science, Djillali Liabes University, Sidi Bel-Abbes, Algeria

2. UEB INSA IETR D├ępartement Image et Automatique, 35708 Rennes, France

3. Department of Electronics, Djillali Liabes University, Sidi Bel-Abbes, Algeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2015.08.01

Received: 6 Mar. 2015 / Revised: 17 Apr. 2015 / Accepted: 28 May 2015 / Published: 8 Jul. 2015

Index Terms

Multi-object tracking, Object representation, Orthogonal matching pursuit, Sparse representation, Classification

Abstract

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.

Cite This Paper

Mohamed ELBAHRI, Kidiyo KPALMA, Nasreddine TALEB, Miloud CHIKR EL-MEZOUAR,"A Novel Object Position Coding for Multi-Object Tracking using Sparse Representation", IJIGSP, vol.7, no.8, pp.1-12, 2015. DOI: 10.5815/ijigsp.2015.08.01

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