基于编码迁移的快速鲁棒视觉跟踪

doi:10.11999/JEIT160966

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Abstract

The sparsity constraint of the L1 tracker’s representation model makes it have good robustness towards partial occlusion. However, the tracking speed of the L1 tracker is slow. To solve this study, this paper proposes a coding transfer method for visual tracking. By making use of the low-resolution dictionary to calculate coefficients of the candidate targets and the high-resolution dictionary to construct the observation likelihood model, the method reduces calculation amount effectively in the process of tracking. In order to improve the precision of coding transfer and the ability of the dictionary to overcome the background clutters, this study proposes an online robust discrimination joint dictionary learning model to update the dictionaries. The experimental results demonstrate that the proposed method has good robustness and superior tracking speed.

Key words： L1 tracker Coding transfer Dictionary learning Particle filter

Received: 26 September 2016 Published: 21 March 2017

PACS:

TP391.4

Fund:

The National Natural Science Foundation of China (61175035, 61379105)

Corresponding Authors: LIU Wenzhuo E-mail: 13945049233@163.com

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	XUE Mogen
	LIU Wenzhuo
	YUAN Guanglin
	QIN Xiaoyan

Cite this article:

XUE Mogen,LIU Wenzhuo,YUAN Guanglin等. Fast Robust Visual Tracking Based on Coding Transfer[J]. JEIT, 2017, 39(7): 1571-1577.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160966 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I7/1571

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