Reconfigurable vision processing system for player tracking in indoor sports

In recent years, there has been an increasing growth of using vision-based systems for tracking the players in team sports to evaluate and enhance their performance. Vision-based player tracking has high computational demands since it requires processing of a huge amount of video data based on the utilization of multiple cameras with high resolution and high frame rates. In this paper, we present a reconfigurable system to track the players in indoor sports automatically without user interaction. The proposed system can process live video data streams from multiple cameras as well as offline data from recorded video files. FPGA technology is used to accelerate this player tracking system by implementing the video acquisition, video preprocessing, player segmentation, and team identification & player detection modules in hardware, realizing a real-time system. The teams are identified and the players' positions are detected based on the colors of their jerseys. The detection results are sent from the FPGA to the host-PC where the players are tracked. Our results show that the achieved average player detection rate is up to 95.5%. The proposed system can process live video data using two GigE Vision cameras with a resolution of 1392×1040 pixels and 30 fps for each camera. A speed-up of 20 is achieved compared to an OpenCV-based software implementation on a host-PC equipped with a 2.93 GHz Intel i7 CPU.