Abstract
The Visual Object Tracking challenge VOT2022 is the tenth annual tracker benchmarking activity organized by the VOT initiative. Results of 93 entries are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2022 challenge was composed of seven sub-challenges focusing on different tracking domains: (i) VOT-STs2022 challenge focused on short-term tracking in RGB by segmentation, (ii) VOT-STb2022 challenge focused on short-term tracking in RGB by bounding boxes, (iii) VOT-RTs2022 challenge focused on “real-time” short-term tracking in RGB by segmentation, (iv) VOT-RTb2022 challenge focused on “real-time” short-term tracking in RGB by bounding boxes, (v) VOT-LT2022 focused on long-term tracking, namely coping with target disappearance and reappearance, (vi) VOT-RGBD2022 challenge focused on short-term tracking in RGB and depth imagery, and (vii) VOT-D2022 challenge focused on short-term tracking in depth-only imagery. New datasets were introduced in VOT-LT2022 and VOT-RGBD2022, VOT-ST2022 dataset was refreshed, and a training dataset was introduced for VOT-LT2022. The source code for most of the trackers, the datasets, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).
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Notes
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The target was sought in a window centered at its estimated position in the previous frame. This is the simplest dynamic model that assumes all positions within a search region containing the target have an equal prior probability.
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Acknowledgements
This work was supported in part by the following research programs and projects: Slovenian research agency research program P2-0214 and project J2-2506. The challenge was sponsored by the Faculty of Computer Science, University of Ljubljana, Slovenia. This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP), in particular in terms of the Wallenberg research arena for Media and Language, and the Berzelius cluster at NSC, both funded by the Knut and Alice Wallenberg Foundation, as well as by ELLIIT, a strategic research environment funded by the Swedish government. Besides, this work was partially supported by the Fundamental Research Funds for the Central Universities (No. 226-2022-00051). This work has also received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 899987. Hyung Jin Chang and Aleš Leonardis were supported by the Institute of Information and communications Technology Planning and evaluation (IITP) grant funded by the Korea government (MSIT) (2021-0-00537). Gustavo Fernández was supported by the AIT Strategic Research Program 2022 Visual Surveillance and Insight.
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Kristan, M. et al. (2023). The Tenth Visual Object Tracking VOT2022 Challenge Results. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13808. Springer, Cham. https://doi.org/10.1007/978-3-031-25085-9_25
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