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Multi-scale kernel correlation filter for visual tracking
Author(s): Faling Chen; Qinghai Ding; Haibo luo; Bin Hui; Zheng Chang; Yunpeng Liu
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Paper Abstract

Correlation filter, previously used in object detection and recognition assignment within single image, has become a popular approach to visual tracking due to its high efficiency and robustness. Many trackers based on the correlation filter, including Minimum Output Sum of Squared Error (MOSSE), Circulant Structure tracker with Kernels (CSK) and Kernel Correlation Filter (KCF), they simply estimate the translation of a target and provide no insight into the scale variation of a target. But in visual tracking, scale variation is one of the most common challenges and it influences the visual tracking performance in stability and accuracy. Thus, it is necessary to handle the scale variation. In this paper, we present an accurate scale estimation solution with two steps based on the KCF framework in order to tackle the changing of target scale. Meanwhile, besides the original pixel grayscale feature, we integrate the powerful features Histogram of Gradient (HoG) and Color Names (CN) together to further boost the overall visual tracking performance. Finally, the experimental results demonstrate that the proposed method outperforms other state-of-the-art trackers.

Paper Details

Date Published: 12 December 2018
PDF: 7 pages
Proc. SPIE 10846, Optical Sensing and Imaging Technologies and Applications, 108461G (12 December 2018); doi: 10.1117/12.2504742
Show Author Affiliations
Faling Chen, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Qinghai Ding, Shenyang Institute of Automation (China)
Space Star Technology Co., Ltd. (China)
Haibo luo, Shenyang Institute of Automation (China)
Bin Hui, Shenyang Institute of Automation (China)
Zheng Chang, Shenyang Institute of Automation (China)
Yunpeng Liu, Shenyang Institute of Automation (China)


Published in SPIE Proceedings Vol. 10846:
Optical Sensing and Imaging Technologies and Applications
Mircea Guina; Haimei Gong; Jin Lu; Dong Liu, Editor(s)

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