Large margin multi-kernel tensor correlation filter for visual tracking

Cong Yu; Guoxia Xu; Yu-Feng Yu; Hao Wang

doi:10.1117/12.2541922

14 February 2020 Large margin multi-kernel tensor correlation filter for visual tracking

Cong Yu, Guoxia Xu, Yu-Feng Yu, Hao Wang

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 1143020 (2020) https://doi.org/10.1117/12.2541922
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Cong Yu, Guoxia Xu, Yu-Feng Yu, and Hao Wang "Large margin multi-kernel tensor correlation filter for visual tracking", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143020 (14 February 2020); https://doi.org/10.1117/12.2541922

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KEYWORDS

Image filtering

Optical tracking

Electronic filtering

Nonlinear filtering

Chemical elements

Detection and tracking algorithms

Computer vision technology

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