Share Email Print
cover

Proceedings Paper

Increment adaptive correlation filter for visual tracking
Author(s): Gang Chen; Zhiwen Fang; Zhou Yue; Bo Liu; Yang Xiao; Yanan Li
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301Q (14 February 2020); doi: 10.1117/12.2541744
Show Author Affiliations
Gang Chen, Hunan Univ. of Humanities, Science and Technology (China)
Zhiwen Fang, Hunan Univ. of Humanities, Science and Technology (China)
Southern Medical Univ. (China)
Zhou Yue, Hunan Univ. of Humanities, Science and Technology (China)
Bo Liu, Hunan Univ. of Humanities, Science and Technology (China)
Yang Xiao, Huazhong Univ. of Science and Technology (China)
Yanan Li, Wuhan Institute of Technology (China)


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

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray