结合PLS表示与随机梯度的目标优化跟踪

doi:10.11999/JEIT151082

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Abstract

In order to improve the stability and accuracy of the object tracking under nonlinear conditions, an object tracking algorithm based on Partial Least Squares (PLS) representation and stochastic gradient object optimization tracking is proposed. In this method, object tracking is defined as an optimization task that minimizes the representation error and classification loss. Firstly, it expresses object appearance and background information by PLS theory, learns multiple low dimensional and discriminative subspaces to describe the nonlinear appearance changes of the object. Then, a joint optimization objective function based on deterministic search mechanism is proposed. Furthermore, an stochastic gradient classifier based on incremental features updating is proposed, and make sure that it can achieve a stable tracking. Experiments show favorable performance of the proposed algorithm on sequences where the targets undergo a variety complex changes on foreground and background.

Key words： Object tracking Partial Least Squares (PLS) Appearance model Stochastic gradient Joint optimization

Received: 23 September 2015 Published: 24 June 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61501470)

Corresponding Authors: JIN Guangzhi E-mail: azhide1025@163.com

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	JIN Guangzhi
	SHI Linsuo
	LIU Hao
	MU Weijie
	CAI Yanping

Cite this article:

JIN Guangzhi,SHI Linsuo,LIU Hao等. Object Optimization Tracking via PLS Representation and Stochastic Gradient[J]. JEIT, 2016, 38(8): 2027-2032.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151082 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I8/2027

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