Adaptive Regularized Correlation Filters for Visual Tracking Based on Sample Quality Estimation

Zhiqiang HOU; Shuai WANG; Xiufeng LIAO; Wangsheng YU; Jiaoyao WANG; Chuanhua CHEN

doi:10.11999/JEIT180921

Volume 41 Issue 8

Aug. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(8): 1983-1991

Zhiqiang HOU, Shuai WANG, Xiufeng LIAO, Wangsheng YU, Jiaoyao WANG, Chuanhua CHEN. Adaptive Regularized Correlation Filters for Visual Tracking Based on Sample Quality Estimation[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1983-1991. doi: 10.11999/JEIT180921

Citation:

Zhiqiang HOU, Shuai WANG, Xiufeng LIAO, Wangsheng YU, Jiaoyao WANG, Chuanhua CHEN. Adaptive Regularized Correlation Filters for Visual Tracking Based on Sample Quality Estimation[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1983-1991. doi: 10.11999/JEIT180921

Citation:

Zhiqiang HOU, Shuai WANG, Xiufeng LIAO, Wangsheng YU, Jiaoyao WANG, Chuanhua CHEN. Adaptive Regularized Correlation Filters for Visual Tracking Based on Sample Quality Estimation[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1983-1991. doi: 10.11999/JEIT180921

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Adaptive Regularized Correlation Filters for Visual Tracking Based on Sample Quality Estimation

doi: 10.11999/JEIT180921

1.
School of Computer Science & Technology, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
2.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
3.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Funds: The National Natural Science Foundation of China (61473309, 61703423)

Received Date: 2018-09-27
Rev Recd Date: 2019-05-20

Available Online: 2019-05-27

Publish Date: 2019-08-01

Abstract

Abstract

Correlation Filters (CF) are efficient in visual tracking, but their performance is badly affected by boundary effects. Focusing on this problem, the adaptive regularized correlation filters for visual tracking based on sample quality estimation are proposed. Firstly, the proposed algorithm adds spatial regularization matrix to the training process of the filters, and constructs color and gray histogram templates to compute the sample quality factor. Then, the regularization term adaptively changes with the sample quality coefficient, so that the samples of different quality are subject to different degrees of punishment. Then, by thresholding the sample quality coefficient, the tracking results and model update strategy are optimized. The experimental results on OTB2013 and OTB2015 indicate that, compared with the state-of-the-art tracking algorithm, the average success ratio of the proposed algorithm is the highest. The success ratio is raised by 9.3% and 9.9% contrasted with Spatially RegularizeD Correlation Filters(SRDCF) algorithm respectively on OTB2013 and OTB2015.
- Visual tracking,
- Correlation Filters (CF),
- Adaptive regularization,
- Sample quality estimatio

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References(25)

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