In field sports, to realize tactical analysis, many techniques for athlete detection, identification and tracking by analyzing videos have been developed. However, many techniques have the same problems that the accuracy of detection, identification and tracking will decline in quality while athletes are occluding. In this research, the authors focus on the fact that the features of each athlete before and after occlusion are approximated, and then developed the method for identifying athletes with their features before and after occlusion. In the results, we were able to solve the problem of degrading detection, identification and tracking accuracy in the occlusion interval.
In recent years, the utilization of ICT for field sports has been promoted in Japan. And then, a lot of researches have been conducted on tactical analysis using positions and trajectories of players obtained from play video data. However, in many of them, an area of an athlete may be misidentified as area containing shadow or area of only shadow, which leads to a problem of acquiring accurate positions of athletes. Therefore, in this research, a method to remove the shadows of players from soccer play videos by using pix2pix, a type of GAN, to generate shadow-free videos is proposed and implemented. As a result, it was verified through experiments that the shadows of athletes could be removed accurately. Furthermore, the effectiveness of the proposed method was also confirmed by tracking accuracy comparison of an existing person tracking method with and without shadow removal.