Alpine Skiing Tracking Method Based on Deep Learning and Correlation Filter

Abstract

In outdoor sports such as alpine skiing, athletes hope to obtain information such as movement trajectory and speed to achieve scientific training. The portable sensor has a sense of restraint and affects the athlete’s performance. Cameras have difficulty capturing the whole race in an outdoor environment. For alpine skiing needs, we designed an alpine skiing information acquisition system, using unmanned aerial vehicle (UAV) and ground cameras to obtain information on the whole path of the race. The environment of the alpine skiing field, the high-speed curvilinear movement of athletes and the flight characteristics of UAVs cause challenges in the detection and tracking of athletes by UAVs. To realize the detection and tracking of alpine skiing by UAVs, we propose a detection algorithm by combining neural networks and correlation filters. A tracking confidence score based on the motion distance of the interval frame is defined. When the tracking confidence of the neural network detector is less than the threshold, the correlation filter is used to expand the recognition range and realize redetection. In addition, we created and annotated a fresh alpine skiing dataset. We compare our algorithm with four advanced algorithms on the UAV123 dataset. Three methods are used to evaluate the performance of our method on the alpine skiing sequence dataset. From the simulation results, our algorithm outperforms the comparison methods in terms of accuracy and robustness. Therefore, our algorithm has application value in the scientific training of alpine skiers.