In order to enhance the intelligent analysis effects in team sports, a unified framework named DeepSportLab was put forward, which combines the principles of part strength field and spacial embedding to train multi-task simultaneously. This framework aims to achieve the basketball detection, player pose estimation, and player instance mask segmentation in sports scenes, solving the complexity and particularity of team movement scene such as strong occlusion and motion blur. Within this framework, the part strength field provides positional information for both the basketball and the players, as well as the locations of the player's joints. Then, the spatial embedding technique was employed to associate each player's instance pixels with their respective center points, and the player's joint points were combined to form skeletal information. This method has been validated on the DeepSport basketball dataset and has achieved good performance comparable to individual models with independent tasks.
关键词
部件强度场/篮球检测/姿态估计/掩码分割
Key words
part intensity field/ball detection/pose estimation/mask segmentation
维普
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