NDLI: Partial Occlusion Handling for Visual Tracking via Robust Part Matching

Content Provider	IEEE Xplore Digital Library
Author	Tianzhu Zhang Kui Jia Changsheng Xu Yi Ma Ahuja, N.
Copyright Year	2014
Description	Author affiliation: Inst. of Autom., Beijing, China (Tianzhu Zhang; Kui Jia; Changsheng Xu; Yi Ma; Ahuja, N.)
Abstract	Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. The proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal locality-constrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) The proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. This contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.
Starting Page	1258
Ending Page	1265
File Size	1291625
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.164
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Visualization Robustness Optimization Noise Video sequences visual tracking
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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