Fast Object Tracking Based on L2-norm Minimization andCompressed Haar-like Features Matching

WU Zhengping; YANG Jie; CUI Xiaomeng; ZHANG Qingnian

doi:10.11999/JEIT160122

Volume 38 Issue 11

Dec. 2016

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WU Zhengping, YANG Jie, CUI Xiaomeng, ZHANG Qingnian. Fast Object Tracking Based on L2-norm Minimization andCompressed Haar-like Features Matching[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2803-2810. doi: 10.11999/JEIT160122

Citation:

WU Zhengping, YANG Jie, CUI Xiaomeng, ZHANG Qingnian. Fast Object Tracking Based on L2-norm Minimization andCompressed Haar-like Features Matching[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2803-2810. doi: 10.11999/JEIT160122

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Fast Object Tracking Based on L2-norm Minimization andCompressed Haar-like Features Matching

doi: 10.11999/JEIT160122

1.
2.
(The Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China)

Funds:

The National Natural Science Foundation of China (51479159)

Received Date: 2016-01-26
Rev Recd Date: 2016-06-08
Publish Date: 2016-11-19

Abstract

Abstract

Under the framework of the Bayesian inference, tracking methods based on PCA subspace and L2-norm minimization can deal with some complex appearance changes in the video scene successfully. However, they are prone to drifting or failure when the target object undergoes pose variation or rotation. To deal with this problem, a fast visual tracking method is proposed based on L2-norm minimization and compressed Haar-like features matching. The proposed method not only removes square templates, but also presents a simple but effective observation likelihood, and its robustness to pose variation and rotation is strengthened by Haar-like features matching. Compared with other popular method, the proposed method has stronger robustness to abnormal changes (e.g. heavy occlusion, drastic illumination change, abrupt motion, pose variation and rotation, etc). Furthermore, it runs fast with a speed of about 29 frames/s.
- Object tracking,
- PCA subspace,
- L2-norm minimization,
- Compressed Haar-like feature,
- Observation likelihood

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References(19)

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