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Self-repairing object tracking method by adopting multi-level features
Author(s): Mengjie Hu; Ying Xiong; Xiaoyang Li
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Paper Abstract

Visual object tracking is a fundamental problem in computer vision community and has been studied for decades. Trackers are prone to drift over time without other information. In this paper, we propose a self-repairing online object tracking algorithm based on different level of features. The fine-grained low-level features are used to locate the specific object in each frame and the coarse-grained high-level features are used to describe the category-level representation. We design a tracking kernel updating mechanism based on category-level description to revise the online tracking drift. We tested our proposed algorithm on OTB-50 dataset and compared the proposed method with some popular real-time online tracking algorithms. Experimental results demonstrated the effectiveness of our proposed method.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211L (27 November 2019); doi: 10.1117/12.2548621
Show Author Affiliations
Mengjie Hu, Beijing Univ. of Posts and Telecommunications (China)
Ying Xiong, Coordination Ctr. of China (China)
Xiaoyang Li, Baidu, Inc. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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