Structural Sparse Tracking

Title
Structural Sparse Tracking
Authors
Yang, Ming-hsuan
Keywords
Target tracking; Dictionaries; Joints; Layout; Computational modeling; Object tracking; Visualization
Issue Date
2015-06
Publisher
IEEE
Citation
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on 2015 June, Page. 150-158
Abstract
Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.
URI
http://ieeexplore.ieee.org/document/7298610/http://hdl.handle.net/20.500.11754/25605
ISBN
978-1-4673-6964-0
ISSN
1063-6919
DOI
http://dx.doi.org/10.1109/CVPR.2015.7298610
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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