Cost-efficient and bias-robust sports player tracking by integrating GPS and video - Kim, H.; Kim, C. J.; Jeong, M.; Lee, J.; Yoon, J.; Ko, S. K. (Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science, 2022)
Player tracking data are now widely used in the sports industry to provide deeper insights to domain participants. Global positioning systems (GPS) and camera-based optical tracking systems (OTS) are two common tracking systems, but the former suffers from location biases and the latter requires either a heavy installment of multiple cameras or a lot of manual correction work. Overcoming these weaknesses of individual systems, we propose a framework for cost-efficient and bias-robust player tracking by integrating GPS and video data. We design a sophisticated filtering algorithm to selectively use the positional information from bounding boxes detected in the video and use the GPS data as a reliable tool for identifying the chosen boxes. Using the player identity and location information of these bounding boxes, we estimate and remove GPS biases in two steps to obtain unbiased player trajectories. We demonstrate that our algorithm precisely tracks players from video with the aid of GPS data even in poor conditions such as the presence of player occlusions and players outside the sight of cameras.