The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

Matej Kristan, Jirí Matas, Martin Danelljan, Michael Felsberg, Hyung Jin Chang, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Zhongqun Zhang, Khanh-Tung Tran, Xuan-Son Vu, Johanna Björklund, Christoph Mayer, Yushan Zhang, Lei Ke, Jie Zhao, Gustavo Fernández, Noor Al-Shakarji, Dong An, Michael Arens et al. (109 additional authors not shown)

Abstract: The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .