Visual Tracking Based on Random Projection and Sparse Representation

YU Daoyin; WANG Yuexing; CHEN Xiaodong; WANG Yi

doi:10.11999/JEIT151064

Volume 38 Issue 7

Jul. 2016

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YU Daoyin, WANG Yuexing, CHEN Xiaodong, WANG Yi. Visual Tracking Based on Random Projection and Sparse Representation[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064

Citation:

YU Daoyin, WANG Yuexing, CHEN Xiaodong, WANG Yi. Visual Tracking Based on Random Projection and Sparse Representation[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064

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Visual Tracking Based on Random Projection and Sparse Representation

doi: 10.11999/JEIT151064

Received Date: 2015-09-21
Rev Recd Date: 2016-04-01
Publish Date: 2016-07-19

Abstract

Abstract

A robust object tracking method is proposed to deal with technical issues during tracking. Firstly, the global template based on sparse representation is used to describe object appearance, while positive and negative modules are built to separate the object from the background. Then, Random Projection (RP) is used to reduce the dimension of modules and candidate objects, which could release the calculation burden. Furthermore, the Particle Filter (PF) is used as the object motion model, and the multi-normal resample method is used to maintain the diversity of particles. To alleviate module drift problem, the positive module is divided into static module and changeable module, while different modules are dealt with different ways, and sparse reconstruction error is used to determine whether the object is occluded. Experiment results on numerous challenging videos show that the proposed method has better performance in accuracy and stability in comparison with state-of-the-art tracking methods.
- Object tracking,
- Sparse representation,
- Random Projection (RP),
- Template updating,
- Resample method

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References(26)

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