Pose Feature Fusion Based Short Track Speed Skaters Multi Objective Tracking

Multi-objective tracking technology is of great significance for data analysis for short track speed skating competitions and for athletes to provide auxiliary technical support. The tracking process is more challenging due to the large scale changes, frequent occlusions, motion blurring and similar appearance of the skaters during the skating. To this end, a multi-objective tracking dataset SSSMOT is constructed for speed skaters in the short track speed skating scene, and a novel multi-objective tracking method is proposed based on pose information. The method firstly optimizes the Yolov5 detection model in terms of Anchor, loss function, and NMS to improve the detection accuracy; secondly, a new feature extraction network P-RNet is designed to extract features based on the pose information to improve the feature robustness; finally, the data association matching method is improved by using the pose key points to alleviate the matching error caused by the similar appearance of athletes to a certain extent. Finally, Using pose key points to improve the data association matching method can alleviate the problem of matching errors caused by similar appearance of athletes to a certain extent. In this paper, experiments are conducted on SSSMOT and SKMOT datasets, and the effectiveness and superiority of the proposed method are demonstrated in a large number of experiments.