融合L2范数最小化和压缩Haar-like特征匹配的快速目标跟踪

doi:10.11999/JEIT160122

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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.

Key words： Object tracking PCA subspace L2-norm minimization Compressed Haar-like feature Observation likelihood

Received: 26 January 2016 Published: 08 September 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (51479159)

Corresponding Authors: YANG Jie E-mail: jieyang509@163.com

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	WU Zhengping
	YANG Jie
	CUI Xiaomeng
	ZHANG Qingnian

Cite this article:

WU Zhengping,YANG Jie,CUI Xiaomeng等. Fast Object Tracking Based on L2-norm Minimization and Compressed Haar-like Features Matching[J]. JEIT, 2016, 38(11): 2803-2810.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160122 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2803

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