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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 todoi:10.1109/access.2019.2913953 fatcat:blouqn6g2rgjpbqknqmqhnc3mu