Object Tracking Method Based on Sparse Optimization of Local Sensing

LIU Daqian; LIU Wanjun; FEI Bowen

doi:10.11999/JEIT170473

Volume 40 Issue 2

Feb. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 272-281

LIU Daqian, LIU Wanjun, FEI Bowen. Object Tracking Method Based on Sparse Optimization of Local Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(2): 272-281. doi: 10.11999/JEIT170473

Citation:

LIU Daqian, LIU Wanjun, FEI Bowen. Object Tracking Method Based on Sparse Optimization of Local Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(2): 272-281. doi: 10.11999/JEIT170473

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Object Tracking Method Based on Sparse Optimization of Local Sensing

doi: 10.11999/JEIT170473

1.
(School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China)
2.
(School of Software, Liaoning Technical University, Huludao 125105,China)
3.
(School of Business and Management, Liaoning Technical University, Huludao 125105, China)

Funds:

The National Natural Science Foundation of China (61172144), The Science and Technology Foundation of Liaoning Province (2012216026)

Received Date: 2017-05-17
Rev Recd Date: 2017-08-01
Publish Date: 2018-02-19

Abstract

Abstract

The problem of tracking drift is produced easily by traditional sparse representation tracking methods in complex scene. To solve this problem, a novel tracking approach based on sparse optimization of local sensing is proposed. Firstly, the object area of the first frame is divided into non-overlapping uniform segmentation, and building the template set using global features and local features. Then, a local sensing correction method for constraining sparse optimization matching process is utilized to determine the optimal matching samples. Finally, a new method of occlusion decision is used to detect occlusion, and updating strategies are adopted according to different occlusion conditions, which makes the template sets more complete in the process of template update. The experiments compare with state-of-the-art tracking algorithms on 10 tracking test sequences of benchmark library. Experiment results indicate that the proposed method possesses characteristics of accurate tracking and strong adaptability in the conditions of partial occlusion, deformation, and complex background.
- Object tracking,
- Local sensing,
- Sparse optimization,
- Occlusion decision

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References(21)

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