Real-time visual tracking using complementary kernel support correlation filters

doi:10.1007/s11704-018-8116-1

Front. Comput. Sci.

2020, Vol. 14

Issue (2) : 417-429 https://doi.org/10.1007/s11704-018-8116-1

RESEARCH ARTICLE

Real-time visual tracking using complementary kernel support correlation filters

Zhenyang SU^1,², Jing LI¹(

), Jun CHANG¹, Bo DU¹, Yafu XIAO¹

¹. School of Computer Science, Wuhan University,Wuhan 430072, China
². Department of Digital Media Technology, Huanggang Normal University, Huangzhou 438000, China

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Abstract

Despite demonstrated success of SVM based trackers, their performance remains a boosting room if carefully considering the following factors: first, the tradeoff between sampling and budgeting samples affects tracking accuracy and efficiency much; second, how to effectively fuse different types of features to learn a robust target representation plays a key role in tracking accuracy. In this paper, we propose a novel SVM based tracking method that handles the first factor with the help of the circulant structures of the samples and the second one by a multi-kernel learning mechanism. Specifically, we formulate an SVM classification model for visual tracking that incorporates two types of kernels whose matrices are circulant, fully taking advantage of the complementary traits of the color and HOG features to learn a robust target representation. Moreover, it is fortunate that the SVM model has a closed-form solution in terms of both the classifier weights and the kernel weights, and both can be efficiently computed via fast Fourier transforms (FFTs). Extensive evaluations on OTB100 and VOT2016 visual tracking benchmarks demonstrate that the proposed method achieves a favorable performance against various state-of-the-art trackers with a speed of 50 fps on a single CPU.

Keywords visual tracking SVM correlation filter multikernel learning

Corresponding Author(s): Jing LI

Just Accepted Date: 09 August 2018 Online First Date: 17 September 2019 Issue Date: 16 October 2019

Cite this article:

Zhenyang SU,Jing LI,Jun CHANG, et al. Real-time visual tracking using complementary kernel support correlation filters[J]. Front. Comput. Sci., 2020, 14(2): 417-429.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-8116-1
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I2/417

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