Increment adaptive correlation filter for visual tracking

Gang Chen; Zhiwen Fang; Zhou Yue; Bo Liu; Yang Xiao; Yanan Li

doi:10.1117/12.2541744

14 February 2020 Increment adaptive correlation filter for visual tracking

Gang Chen, Zhiwen Fang, Zhou Yue, Bo Liu, Yang Xiao, Yanan Li

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301Q (2020) https://doi.org/10.1117/12.2541744
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Currently, the correlation filter is widely used in visual tracking because of its effectiveness and efficiency. To adapt the representation to changing target appearances, a linear interpolation is used to update tracking models according to a manually designed learning rate. However, The limitation of manually tricks make methods only apply to some special scenes because the threshold parameters are sensitive to different response maps in complex scenes. In this paper, to overcome this problem, an adaptive increment correlation filter based tracker is proposed. Different from traditional linear interpolation depending on a manual learning rate, the increment is learned by linear regression based on the history tracking model and the current training samples. Experimentally, we show that our algorithm can outperform state-of-the-art key point-based trackers.

Citation Download Citation

Gang Chen, Zhiwen Fang, Zhou Yue, Bo Liu, Yang Xiao, and Yanan Li "Increment adaptive correlation filter for visual tracking", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301Q (14 February 2020); https://doi.org/10.1117/12.2541744

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KEYWORDS

Optical tracking

Digital filtering

Image filtering

Electronic filtering

Statistical modeling

Visual process modeling

Navigation systems

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