Visual tracking using discriminative representation with <i>ℓ</i><sub>2</sub> regularization

doi:10.1007/s11704-017-6434-9

Front. Comput. Sci.

2019, Vol. 13

Issue (1) : 199-211 https://doi.org/10.1007/s11704-017-6434-9

RESEARCH ARTICLE

Visual tracking using discriminative representation with ℓ₂ regularization

Haijun WANG^1,²(

), Hongjuan GE¹

¹. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
². Aviation Information Technology Research and Development Center, Binzhou University, Binzhou 256603, China

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Abstract

In this paper, we propose a novel visual tracking method using a discriminative representation under a Bayesian framework. First, we exploit the histogram of gradient (HOG) to generate the texture features of the target templates and candidates. Second, we introduce a novel discriminative representation and ℓ₂-regularized least squares method to solve the proposed representation model. The proposed model has a closed-form solution and very high computational efficiency. Third, a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error.

Keywords visual tracking discriminative representation Bayesian framework closed-form solution

Corresponding Author(s): Haijun WANG

Just Accepted Date: 20 January 2017 Online First Date: 06 March 2018 Issue Date: 31 January 2019

Cite this article:

Haijun WANG,Hongjuan GE. Visual tracking using discriminative representation with ℓ₂ regularization[J]. Front. Comput. Sci., 2019, 13(1): 199-211.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6434-9
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I1/199

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