Multi-object tracking using sparse representation

Weizhi Lu (1) , Cong Bai (1) , Kidiyo Kpalma (1) , Joseph Ronsin (1)

1 IETR - Institut d'Électronique et des Technologies du numéRique

Recently sparse representation has been successfully applied to single object tracking by observing the reconstruction error of candidate object with sparse representation. In practice, sparse representation also shows competitive performance on multi-class classification, and thus is potential for multi-object tracking. In this paper we explore this technique for on-line multi-object tracking through a simple trackingby- detection scheme, with background subtraction for object detection and sparse representation for object recognition. Final experiments demonstrate that the proposed approach only combining color histogram and 2-dimensional coordinates as features, achieves favorable performance over state-of-the-art work in persistent identity tracking.

Mots clés: multi-object; tracking; sparse representation

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

Weizhi Lu, Cong Bai, Kidiyo Kpalma, Joseph Ronsin. Multi-object tracking using sparse representation. ICASSP 2013, May 2013, Vancouver, Canada. 5 p. ⟨hal-00802986⟩