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6 May 2019 Refining initial bounding-box for robust visual tracking
Wangsheng Yu, Xianxiang Qin, Peng Wang, Zhiqiang Hou
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Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 1106942 (2019) https://doi.org/10.1117/12.2524220
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China
Abstract
Most of the visual tracking algorithms are very sensitive to the initialized bounding-box of the tracking object, while, how to obtain a precise bounding-box in the first frame needs further research. In this paper, we propose an automatic algorithm to refine the references of the tracking object after a roughly selected bounding-box in the first frame. Based on the input rough location and scale information, the proposed algorithm exploits the region merger algorithm based on maximal similarity to segment the superpixel regions into foreground or background. In order to improve the segmentation effect, a feature clustering strategy is exploited to obtain reliable foreground label and background label and color histogram in HSI space is exploited to describe the superpixel feature. The final refinement bounding-box is the minimal enclosing rectangle of the foreground region. Extensive experiments are performed and the results indicate that the proposed algorithm can reliably refine the initial bounding-box relying only on the first frame information and improve the robustness of the tracking algorithms distinctively.
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Wangsheng Yu, Xianxiang Qin, Peng Wang, and Zhiqiang Hou "Refining initial bounding-box for robust visual tracking", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106942 (6 May 2019); https://doi.org/10.1117/12.2524220
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KEYWORDS
Detection and tracking algorithms
Image segmentation
Optical tracking
Particle filters
Image processing algorithms and systems
Automatic tracking
Evolutionary algorithms
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