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