Tracking and identification for football video analysis using deep learning

Abstract

We describe the technique used to train and customize deep learning models to detect, track, and identify soccer players, who are recorded during soccer games using custom camera settings. The player detection model is customized to allow the detection of person class objects from video input. Two newly developed filters, spatial feature filters, and bounding box location filters have described that help in classifying players and audiences. A new tacking paradigm is illustrated to generate tracks of soccer players with fewer swaps, thereby reducing efforts of human annotators in later stages. A new method of identifying every player by detecting player t-shirt numbers has been developed and illustrated. This method provides tracks with high confidence and identity to most of the player corresponding to individual t-shirt number. Finally, we provide a unique result assessment technique to judge the performance of the complete model.