Roboflow Sports is a computer vision toolkit for applying object detection, image segmentation, keypoint detection, and foundational models to sports analytics challenges.
The toolkit addresses core problems in sports video analysis where precision and timing are critical. It tackles ball tracking despite small size and rapid motion, jersey number recognition under poor visibility conditions, consistent player identification across occlusions, player re-identification when entering and leaving frame, and camera calibration for extracting advanced statistics like player speed and distance. The project provides reusable tools and datasets that can be applied both within sports and to similar computer vision problems in other domains.
The toolkit suits teams building sports analytics systems who need to handle soccer and basketball footage. It provides datasets for soccer player detection, soccer ball detection, soccer pitch keypoint detection, basketball court keypoint detection, and basketball jersey number recognition. The project is designed as a testing ground for pushing computer vision models to their limits under real-world sports conditions, making it relevant for anyone working with high-resolution video, small object tracking, or player-level analytics.
The project actively solicits community contributions and maintains an open issue tracker for feature requests. Development centers on expanding the toolkit's capabilities to handle the specific challenges of sports video analysis.
