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    题名: The Eighth Visual Object Tracking VOT2020 Challenge Results
    作者: Kristan, Matej;Leonardis, Ales;Matas, Jiri;Felsberg, Michael;Pflugfelder, Roman P.;etc
    关键词: Visual object tracking;Performance evaluation protocol;State-of-the-art benchmark;RGB;RGBD;Depth;RGBT;Thermal imagery;Short-term trackers;Long-term trackers
    日期: 2020-08-23
    上传时间: 2021-09-02 12:13:11 (UTC+8)
    摘要: The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).
    DOI: 10.1007/978-3-030-68238-5_39
    显示于类别:[電機工程學系暨研究所] 會議論文

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