Abstract
Unmanned aerial vehicle (UAV)-based target tracking is a long-standing problem in UAV applications. In this paper, we develop a local kernel feature to encode properties of UAV tracking object. Meanwhile, object proposals can provide a reliable prior knowledge to identify tracking target being an object or not. Therefore, we propose to integrate detection proposal method into a tracking by detection framework. More specifically, we adopt edge box proposals and random samplings as training examples and then train these examples for tracking task. The structured support vector machine is employed to implement training and detecting procedure. To reveal the effectiveness of our method, experiment is performed on the UAV123 benchmark dataset. Among state-of-the-art methods, our method achieves comparable results.
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This paper is jointly supported by the National Natural Science Foundation of China No. 61374161, China Aviation Science Foundation 20142057006, Chinese special research program of a large civil aircraft under the Grant MJZ-2016-S-04.
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Wang, Y., Luo, X., Ding, L. et al. Adaptive sampling for UAV tracking. Neural Comput & Applic 31, 5029–5043 (2019). https://doi.org/10.1007/s00521-018-03996-8
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DOI: https://doi.org/10.1007/s00521-018-03996-8