FootAndBall: Integrated player and ball detector
This addresses the need for efficient, real-time object detection in sports analytics, specifically for soccer, but is incremental as it builds on existing Feature Pyramid Network designs.
The paper tackles the problem of detecting players and the ball in high-resolution soccer match videos by introducing FootAndBall, a specialized deep neural network detector that achieves real-time processing with two orders of magnitude fewer parameters than generic detectors like SSD or YOLO.
The paper describes a deep neural network-based detector dedicated for ball and players detection in high resolution, long shot, video recordings of soccer matches. The detector, dubbed FootAndBall, has an efficient fully convolutional architecture and can operate on input video stream with an arbitrary resolution. It produces ball confidence map encoding the position of the detected ball, player confidence map and player bounding boxes tensor encoding players' positions and bounding boxes. The network uses Feature Pyramid Network desing pattern, where lower level features with higher spatial resolution are combined with higher level features with bigger receptive field. This improves discriminability of small objects (the ball) as larger visual context around the object of interest is taken into account for the classification. Due to its specialized design, the network has two orders of magnitude less parameters than a generic deep neural network-based object detector, such as SSD or YOLO. This allows real-time processing of high resolution input video stream. Our code and pre-trained model can be found on the project website: https://github.com/jac99/FootAndBall .