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Proceedings Paper

Object tracking via kernel-based forward-backward keypoint matching
Author(s): Qi Zhao; Zhiying Du; Hong Zhang; Ding Yuan; Mingui Sun
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Paper Abstract

Object tracking is a challenging research task due to target appearance variation caused by deformation and occlusion. Keypoint matching based tracker can handle partial occlusion problem, but it’s vulnerable to matching faults and inflexible to target deformation. In this paper, we propose an innovative keypoint matching procedure to address above issues. Firstly, the scale and orientation of corresponding keypoints are applied to estimate the target’s status. Secondly, a kernel function is employed in order to discard the mismatched keypoints, so as to improve the estimation accuracy. Thirdly, the model updating mechanism is applied to adapt to target deformation. Moreover, in order to avoid bad updating, backward matching is used to determine whether or not to update target model. Extensive experiments on challenging image sequences show that our method performs favorably against state-of-the-art methods.

Paper Details

Date Published: 8 February 2017
PDF: 5 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022504 (8 February 2017); doi: 10.1117/12.2266440
Show Author Affiliations
Qi Zhao, BeiHang Univ. (China)
Zhiying Du, BeiHang Univ. (China)
Hong Zhang, BeiHang Univ. (China)
Ding Yuan, BeiHang Univ. (China)
Mingui Sun, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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