Online multi-object tracking via robust collaborative model and sample selection

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• 作者：Mohamed A. Naiel^a ; ^{m_naiel@ece.concordia.ca} ; M. Omair Ahmad ; ^a ; ^{omair@ece.concordia.ca} ; M.N.S. Swamy^a ; ^{swamy@ece.concordia.ca} ; Jongwoo Lim^b ; ^{jlim@hanyang.ac.kr} ; Ming-Hsuan Yang^c ; ^{mhyang@ucmerced.edu}
• 关键词：Multi-object tracking ; Particle filter ; Collaborative model ; Sample selection ; Sparse representation
• 刊名：Computer Vision and Image Understanding
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：154
• 期：Complete
• 页码：94-107
• 全文大小：3838 K
• 卷排序：154

文摘

The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a collaborative model between a pre-trained object detector and a number of single-object online trackers within the particle filtering framework. For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker. We present a motion model that incorporates the associated detections with object dynamics. Furthermore, we propose an effective sample selection scheme to update the appearance model of each tracker. We use discriminative and generative appearance models for the likelihood function and data association, respectively. Experimental results show that the proposed scheme generally outperforms state-of-the-art methods.