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

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

Автор: Mohamed ELBAHRI, Kidiyo KPALMA, Nasreddine TALEB, Miloud CHIKR EL-MEZOUAR

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 8 vol.7, 2015 года.

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

Короткий адрес: https://sciup.org/15013894

IDR: 15013894

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