Research for Identification and Positional Analysis of American Football Players Using Multi-Cameras from Single Viewpoint

Abstract

Our country is promoting supporting activities for training players and improving the competitiveness in preparation for international sporting events including Tokyo Olympic Games. Especially recently, one of the supporting activities at present is to utilize high-performance sensing devices for analyzing tactics by measuring the positional data of the players and digitizing their performances. However, sensing devices have not been put to practical use yet because it is hard for the players to wear them during games of various kinds of sports. Therefore, technologies for identifying players by image processing from the video images are the mainstream of the sports information fields. However, they have a problem that the identification accuracy decreases because lots of players are occluded into image data. Thus, in this research, we use multi-cameras from single viewpoint to identify American football players with high accuracy and specify their positions by photographing occlusion points with high zoom magnification, and then applying deep learning of the OpenPose. Finally, we embed the identification results of the OpenPose to the video images taken with low zoom magnification and try to identify players and to analyze their positions.