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With the recent development of Deep Learning applied to Computer Vision,sport video understanding has gained a lot of attention, providing much richerinformation for both sport consumers and leagues. This paper introducesDeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarksfor automated sport understanding. The main purpose of this framework is toclose the gap between academic research and real world settings. To this end,the datasets provide high-resolution raw images, camera parameters and highquality annotations. DeepSportradar currently supports four challenging tasksrelated to basketball: ball 3D localization, camera calibration, playerinstance segmentation and player re-identification. For each of the four tasks,a detailed description of the dataset, objective, performance metrics, and theproposed baseline method are provided. To encourage further research onadvanced methods for sport understanding, a competition is organized as part ofthe MMSports workshop from the ACM Multimedia 2022 conference, whereparticipants have to develop state-of-the-art methods to solve the above tasks.The four datasets, development kits and baselines are publicly available.

DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations