Abstract
The existing motion recognition system has a low athlete tracking recognition accuracy due to the poor processing effect of recognition algorithm for edge detection. A machine vision-based gymnast pose-tracking recognition system is designed for the above problem. The software part mainly optimizes the tracking recognition algorithm and uses the spatiotemporal graph convolution algorithm to construct the sequence graph structure of human joints, completes the strategy of label subset division, and completes the pose tracking according to the change of information dimension. The results of the system performance test show that the designed machine vision-based gymnast posture tracking recognition system can enhance the accuracy of tracking recognition and reduce the convergence time compared with the original system.
Highlights
IntroductionThe body data of athletes are very important, especially gymnasts
In sport competitions, the body data of athletes are very important, especially gymnasts
With the development of technology, some posture tracking recognition systems gradually applied in the fitness gymnastics competition and showed a certain effect. e existing system is relatively fast and effective in the process of athlete limb monitoring, but due to the existence of various factors in the process of athletes in the competition, fitness gymnastics in the process of the competition in general, there are many scores, and when the athlete’s posture is detected, the regions with large pixel values in the image will respond, resulting in detection errors in the process of tracking the athlete. e athlete tracking recognition accuracy is low [5]
Summary
The body data of athletes are very important, especially gymnasts. The inertial sensor technology is applied to the pose recognition in basketball and in order to improve the accuracy of the pose solution and reduce the noise interference in the ready detection process, this paper applies the extended Kalman filter algorithm to the pose solution and realizes the data fusion of multiple sensors; in order to solve the problems of complex and space limitation of optical motion capture data processing in sports training, this paper builds a virtual human model in the upper body to realize the human body [6]. In order to solve the problems of complex optical motion capture data processing and space limitation in sports training, this paper builds a virtual human model in the upper body to realize the reproduction of human motion posture and initially completes the display of human lower limb movement and upper limb movement; based on the above research, the recognition method of basketball motion posture is proposed
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