Robust Object Tracking Based on Local Discriminative Analysis

TIAN Peng; Lü Jianghua; MA Shilong; WANG Ronghe

doi:10.11999/JEIT170045

Volume 39 Issue 11

Nov. 2017

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TIAN Peng, Lü Jianghua, MA Shilong, WANG Ronghe. Robust Object Tracking Based on Local Discriminative Analysis[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2635-2643. doi: 10.11999/JEIT170045

Citation:

TIAN Peng, Lü Jianghua, MA Shilong, WANG Ronghe. Robust Object Tracking Based on Local Discriminative Analysis[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2635-2643. doi: 10.11999/JEIT170045

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Robust Object Tracking Based on Local Discriminative Analysis

doi: 10.11999/JEIT170045

Funds:

The National Natural Science Foundation of China (61300007)

Received Date: 2017-01-02
Rev Recd Date: 2017-07-20
Publish Date: 2017-11-19

Abstract

Abstract

The local similarity measurements are usually used for improving the tracking robustness under the complex scene. However, this method have drawbacks in cases of partial occlusion, deformation and rotation. For example, the method only considers traditional similarity measurements of targets and templates, results in the matching errors to lead to tracking failure. In this paper, a target tracking algorithm is proposed based on measurements of the local difference similarities. The presented method has the following advantages: firstly, both similarities and differences are considered for measurement; secondly, the differential weight learning of the local region is carried out to improve the accuracy of sub-block difference measurement; at last, an effective and efficient tracker is designed based on the difference analysis and a simple update manner within the particle filter framework. Experimental results show that the proposed algorithm achieves better performance than traditional competing methods in various factors, such as illumination changes, part occlusion, scale changes and so on.
- Object tracking,
- Local sparse representation,
- Similarity measurement,
- Discriminative analyses,
- Difference weight

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References(25)

References

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