Analyzing Basketball Movements and Pass Relationships using Realtime Object Tracking Techniques based on Deep Learning

In this paper, we present techniques for automatically classifying players and tracking ball movements in basketball game video clips under poor conditions, where the camera angle dynamically shifts and changes. In the core of our system lies Yolo, a realtime object detection system. Given the ground truth boxes collected by our data specialists, Yolo is trained to detect the presence of objects in every video frame. In addition, Yolo uses Darknet that implements convolution neural networks to classify a detected object to a player and to recognize its jersey numbers of specific movements. By identifying players and ball possessions, we can automatically compute ball distributions that are reflected on complex networks. With original Yolo system, player movement can be interrupted, when the players move out of the frame due to camera shift and when players overlap each other on a two-dimensional frame. We have adapted Yolo to keep track of players even under such poor condition by considering contextual information available from the framework preceding and/or succeeding problematic video frames. In addition to the novel movement inference method, we provide a framework for analyzing the pass networks in various perspectives to help the managing staff to reveal critical determinants of team performance and to design better game strategies. We assess the performance of our system in terms of accuracy by making a comparison with the analytical reports generated by human experts.