DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations
This work addresses the need for high-quality, annotated datasets in sports video analysis for researchers and practitioners, though it is incremental as it builds on existing efforts in sports understanding datasets.
The paper introduces DeepSportradar-v1, a dataset suite for computer vision tasks in sports understanding, specifically targeting basketball with tasks like ball 3D localization and player re-identification, and includes public datasets and benchmarks to bridge academic research with real-world applications.
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.