Research on aerobics motion analysis and edge computing methods based on posture estimation

Computer vision technology has been widely used in various fields, and the application of human posture estimation has become an important research topic in computer vision engineering. The problem of human motion similarity evaluation in the field of human motion recognition and analysis, especially the problem of human motion similarity evaluation under different perspectives, lacks effective solutions. Designing a low-complexity human motion behavior analysis algorithm has become an important research topic. This paper uses the Jetson Xavier nx edge computing platform as the computing carrier to propose a human motion posture analysis algorithm based on deep learning posture estimation, increase the posture estimation network experience, reduce the number of floating-point operations and improve the detection efficiency. The extracted human depth feature information is integrated and enhanced to improve the detection accuracy in complex environments. The algorithm is deployed in mobile edge devices to accelerate the human motion analysis processing process. Experimental results show that the detection accuracy of the algorithm in common human motion analysis tasks reaches 90.9%, and the detection speed reaches 77.6FPS, which can generate more reliable similarity evaluation results close to human subjective perception.