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27 November 2019 Self-repairing object tracking method by adopting multi-level features
Mengjie Hu, Ying Xiong, Xiaoyang Li
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Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113211L (2019) https://doi.org/10.1117/12.2548621
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China
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.
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Mengjie Hu, Ying Xiong, and Xiaoyang Li "Self-repairing object tracking method by adopting multi-level features", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211L (27 November 2019); https://doi.org/10.1117/12.2548621
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KEYWORDS
Detection and tracking algorithms
Image filtering
Feature extraction
Visualization
Computer vision technology
Machine vision
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