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Robust UAV-based tracking using hybrid classifiers

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Abstract

Visual object tracking is a key factor for unmanned aerial vehicles. In this paper, we propose a robust and effective visual object tracking method with an appearance model based on the locally adaptive regression kernel. The proposed appearance model encodes the geometric structure of the target. The tracking problem is formulated as two binary classifiers via two support vector machines (SVMs) with online model update. The backward tracking which tracks the target in reverse of time is utilized to evaluate the accuracy and robustness of the two SVMs. The final locations are adaptively fused based on the results of the forward tracking and backward tracking validation. Several state-of-the-art tracking algorithms are evaluated on large-scale benchmark datasets which include challenging factors such as heavy occlusion, pose variation, illumination variation and motion blur. Experimental results demonstrate that our method achieves appealing performance.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Yong Wang

  2. INSKY Lab, Leotail Intelligent Tech, 1628, Pudong District, Shanghai, China

    Wei Shi

  3. Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems, University of Pittsburgh, Pittsburgh, USA

    Shandong Wu

Authors
  1. Yong Wang
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  2. Wei Shi
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  3. Shandong Wu
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Correspondence to Shandong Wu.

Additional information

This work was partially supported by a National Institutes of Health (NIH)/National Cancer Institute (NCI) R01 Grant (#1R01CA193603).

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Wang, Y., Shi, W. & Wu, S. Robust UAV-based tracking using hybrid classifiers. Machine Vision and Applications 30, 125–137 (2019). https://doi.org/10.1007/s00138-018-0981-4

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