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Proceedings Paper

Large margin multi-kernel tensor correlation filter for visual tracking
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Paper Abstract

Visual tracking plays an important role in computer vision research in these years. Multi-kernel correlation filter has demonstrated its outstanding advantage via introducing high level representation from multi-kernel. However, the unskillful selection of multi-kernel inevitably brings redundancy and noise within learning and updating procedure, which significantly affects the accuracy of tracking. A large margin multi-kernel tensor correlation filter for visual tracking (LMKCF) is proposed in this paper. The LMKCF mainly mitigates the redundancy and noise of multi-kernel correlation filter in learning and updating from two aspects with the low rank tensor learning to establishes a prospective learning and updating strategy. And the optimization problem can be solved effectively by the alternating direction method of multipliers (ADMM) method. Last, we validate the proposed tracker with the multi-kernel representations based on OTB benchmark to demonstrate the superiority of the method.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143020 (14 February 2020); doi: 10.1117/12.2541922
Show Author Affiliations
Cong Yu, Nanjing Univ. of Finance and Economics (China)
Guoxia Xu, Guangzhou Univ. (China)
Yu-Feng Yu, Guangzhou Univ. (China)
Hao Wang, Norwegian Univ. of Science and Technology (Norway)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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