A Survey of Advanced Computer Vision Techniques for Sports
This work addresses the problem of automating data collection and analysis for sports performance improvement, but it is incremental as it builds on existing computer vision methods.
The paper surveys computer vision techniques for sports, focusing on object detection and pose estimation to gather data, and presents a model for shot speed estimation using pose data, achieving a correlation of 67%.
Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation of 67%. The possibility of estimating shot speeds enables much deeper studies about enabling the creation of new metrics and recommendation systems that will help athletes improve their performance, in any sport. The proposed methodology is easily replicable for many technical movements and is only limited by the availability of video data.