2020 年 8 巻 2 号 p. 81-88
For automated game analysis, it is essential to detect the kicking motions of players in soccer videos in order to understand each player's actions. This paper presents a fast and accurate approach to detecting kicking motions with a ball-centric window in multi-view 4K soccer videos. Based on powerful object detection techniques like SSD or YOLOv3 and pose estimation techniques like OpenPose or CPN, we propose novel solutions to overcome two challenges in 4K soccer videos. The first challenge is that it is basically too computationally heavy to process the massive amount of data in multi-view 4K videos. The solution to this challenge is that we only process a small portion (i.e. a ball-centric window) of 4K video, benefiting from an object tracking technique and homography transformation. The second challenge is that kicking motions may be incorrectly detected due to two factors. One is the absence of depth information and the other is the inaccuracy of pose estimation. We fuse multiple views to avoid the depth problem. In addition, we propose enlarging the person areas to effectively improve the accuracy of pose estimation. The experiments on real data from the J1 League demonstrate that the proposed approach achieves both faster and more accurate detection of kicking motions than conventional methods.
