A Novel Object Position Coding for Multi-Object Tracking using Sparse Representation
Mohamed Elbahri, Kidiyo Kpalma, Nasreddine Taleb, Miloud Chikr El-Mezouar · International Journal of Image Graphics and Signal Processing · 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.