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).

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The Eighth Visual Object Tracking VOT2020 Challenge Results
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding

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Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part V

Aug 2020

776 pages

ISBN:978-3-030-68237-8

DOI:10.1007/978-3-030-68238-5

Editors:
Adrien Bartoli
University of Clermont Auvergne, Clermont Ferrand, France
,
Andrea Fusiello
Università degli Studi di Udine, Udine, Italy

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Published: 23 August 2020

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Kristan MLeonardis AMatas JFelsberg MPflugfelder RKämäräinen JChang HDanelljan MZajc LLukežič ADrbohlav OBjörklund JZhang YZhang ZYan SYang WCai DMayer CFernández GBen KBhat GChang HChen GChen JChen SChen XChen XChen XChen YChen YChen ZCheng YCiaramella ACui YDžubur BDasari MDeng QDhar DDi SNardo EDu DDunnhofer MFan HFeng ZFu ZGao SGorthi RGranger EGu QGupta HHe JHe KHuang YJangid DJi RJiang CJiang YLawin FKang ZKiran MKittler JLai SLan XLee DLee HLee SLi HLi MLi WLi XLi XLi XLi ZLin LLing HLiu BLiu CLiu SLu HCruz RMa BMa CMa JMa YMartinel NMemarmoghadam AMicheloni CMoallem PNguyen-Meidine LPan SPark CPaudel DPaul MPeng HRobinson ARout LShan SSimonato KSong TSong XSun CSun JTang ZTimofte RTsai CGool LVerma OWang DWang FWang LWang LWang LWang LWang QWu GWu JWu XXie FXu TXu WXu YXu YXue WXun ZYan BYang DYang JYang WYang XYang YYang YYang ZYe BYu FYu HYu JYu QYu WZe KZhai JZhang CZhang CZhang KZhang TZhang WZhang ZZhang ZZhao JZhao SZheng FZheng HZheng MZhong BZhu JZhu XZhuang Y(2022)The Tenth Visual Object Tracking VOT2022 Challenge ResultsComputer Vision – ECCV 2022 Workshops10.1007/978-3-031-25085-9_25(431-460)Online publication date: 23-Oct-2022
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Matej Kristan

University of Ljubljana, Ljubljana, Slovenia

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