Abstract
In order to help badminton players make reasonable training plans and realize a comprehensive grasp of the training process, this paper mainly recognizes and perceives the posture of badminton athletes based on the method of moving edge calculation. Firstly, from the perspective of moving edge motion analysis, considering the vector field formed by moving edge vector as movable spatial distribution information, the spatial distribution model of moving edge field is realized. Secondly, while athletes interact with the computer through limb movement, the overall posture of athletes is divided into several parts, and each part is perceived separately. Finally, in the human posture evaluation module, an algorithm for human posture evaluation in the image pixel plane is proposed. Through comparative experiments, the motion recognition algorithm can effectively recognize the three typical swing movements of badminton players in the video and improve the overall performance of the existing recognition algorithms.
Highlights
Collecting data in sports and analyzing the data to complete posture perception is a hot spot in the intellectualization of the sports industry in recent years
In terms of the fineness of visual content analysis, the movement trajectory information of badminton players can be regarded as a coarse-grained description, which is the macroperformance of the whole game. e attitude perception of badminton players can be regarded as a finegrained description, which can embody the details of the game [3, 4]
In racket motion recognition based on image and video data acquisition, [9] established an event hiding Markov model with binary classification according to the position of players on the court
Summary
Collecting data in sports and analyzing the data to complete posture perception is a hot spot in the intellectualization of the sports industry in recent years. In literature [6], three-axis acceleration sensors are used to collect user action data, and gesture recognition is realized for time-series modeling based on the Hidden Markov model. Literature [7] uses principal component analysis to perceive human posture based on the data collected by the three-axis acceleration sensor. Is paper will take badminton game video as the research object to track and recognize the badminton players’ swing in time-series images. Based on the idea of local motion vision analysis and grid classification, this paper proposes a motion descriptor based on moving edge calculation and constructs an athlete’s attitude perception and evaluation algorithm to classify and recognize three typical swing movements of badminton players [12]. Based on the idea of local motion vision analysis and grid classification, this paper proposes a motion descriptor based on moving edge calculation and constructs an athlete’s attitude perception and evaluation algorithm to classify and recognize three typical swing movements of badminton players [12]. e first part is the introduction, which mainly introduces the research significance and research status at home and abroad. e second part is the swing motion perception based on moving edge calculation, which mainly focuses on the perception, segmentation, and recognition of badminton players’ swing motion. e third part is the posture evaluation of badminton players, which mainly evaluates the perception of human posture, which helps athletes improve the standard of movement. e fourth part is the experimental results and analysis, and the fifth part is the summary and prospect of the full text
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