Object tracking via kernel-based forward-backward keypoint matching

Qi Zhao; Zhiying Du; Hong Zhang; Ding Yuan; Mingui Sun

doi:10.1117/12.2266440

8 February 2017 Object tracking via kernel-based forward-backward keypoint matching

Qi Zhao, Zhiying Du, Hong Zhang, Ding Yuan, Mingui Sun

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022504 (2017) https://doi.org/10.1117/12.2266440
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

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.

Citation Download Citation

Qi Zhao, Zhiying Du, Hong Zhang, Ding Yuan, and Mingui Sun "Object tracking via kernel-based forward-backward keypoint matching", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022504 (8 February 2017); https://doi.org/10.1117/12.2266440

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