Efficient visual tracking by using LBP descriptor

Authors:
Minglei Tong;Hong Han;Jingsheng Lei
Affiliations:
School of Computer and Information, Shanghai University of Electric Power, Shanghai, China;School of Computer and Information, Shanghai University of Electric Power, Shanghai, China;School of Computer and Information, Shanghai University of Electric Power, Shanghai, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

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Abstract

Visual object tracking is a hard problem in many applications for example in video surveillance, human computer interaction(HCI), video communication and compression, augmented reality, traffic control, sports analysis and video editing. The common works towards this task are the ambiguity existing among object and the background because of the moving object and the changing illumination. To track object from cluttered background, LBP descriptor (Local Binary Patterns) is applied in this paper to enable the efficient tracking-by-detection. LBP descriptors are extracted only in region of interest in each frame, to ensure the tracker's high efficiency. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. The dynamic template updating scheme keeps track of the most representative particles throughout the tracking procedure. Experimental results demonstrate the efficiency of the proposed tracker.