Human pose estimation and action recognition for fitness movements

Abstract

Human pose estimation and action recognition have important applications in the areas of security, medicine and sports. In this work, we propose an improved YOLOv7-Pose algorithm to solve the problem of human posture and action recognition for fitness movements in various scenarios. For the algorithm based on YOLOv7-Pose, we add the function of classification to the original network. And we introduce Coordinate Attention to the network to improve the model’s ability to identify important features of human skeletal joints and action classifications. The improved ConvNeXt network structure is introduced to replace the CBS convolution kernel of the original model to improve the accuracy of human key point detection and action classification of the model. We optimise the spatial pyramid pool structure, which can reduce the loss function and accelerate the convergence rate of the model. We adopt EIOU as the regression function of the target detection frame to improve the accuracy of the coordinate regression. Experiments show that the improved YOLOv7-Pose has an mAP of 95.9% on a homemade test set of fitness actions, which is 5.4% higher than HRNet, with a 4.2% improvement over the original YOLOv7 algorithm, suggesting that the accuracy of action recognition and key point estimation has significantly improved.