Fast object tracking based on L2-norm minimization and compressed Haar-like features matching

被引：0

作者：

Wu Z. ^{[1
,2
]}

Yang J. ^{[1
]}

Cui X. ^{[1
]}

Zhang Q. ^{[1
]}

机构：

[1] The Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan

[2] College of Computer and Information Technology, China Three Gorges University, Yichang

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2016年 / 38卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Compressed Haar-like feature; L2-norm minimization; Object tracking; Observation likelihood; PCA subspace;

D O I：

10.11999/JEIT160122

中图分类号：

学科分类号：

摘要：

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. © 2016, Science Press. All right reserved.

引用

页码：2803 / 2810

页数：7

共 19 条

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