Abstract: The training actions of skiing athletes can directly reflect their level, but traditional methods for identifying and evaluating actions have shortcomings such as subjectivity and low accuracy. To achieve accurate analysis of skiing posture, a motion analysis algorithm based on improved OpenPose and YOLOv5(You Only Look Once version 5) is proposed to analyze athletes爷 movements. There are two main improvements. First, CSP-Darknet53(Cross Stage Paritial-Network 53) is used as the external network for OpenPose to reduce the dimension of the input image and extract the feature map. Then, the YOLOv5 algorithm is fused to optimize it. The key points of the human skeleton are extracted to form the human skeleton and compared with the standard action. According to the angle information, the loss function is added to the model to quantify the error between the actual detected action and the standard action. This model achieves accurate and real-time monitoring of athlete action evaluation in training scenarios and can complete preliminary action evaluation. The experimental results show that the detection and recognition accuracy reaches 95%, which can meet the needs of daily skiing training. 

Key words: OpenPose; you only look Once version 5(YOLOv5), deep learning, skiing movement analysis; loss function