Research on basketball players’ action recognition based on interactive system and machine learning

Jin, Li; Gu, Deping

doi:10.3233/jifs-189205

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…e above method is experimentally validated using a publicly available dataset on the Web [24][25][26][27]. e dataset is obtained from the LFMCW radar, which detects four types of human gestures: walking, sitting, standing, and falling.…”

Section: Experiments and Analysismentioning

confidence: 99%

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Yuan

Zheng

2022

Mobile Information Systems

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With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.

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Section: Experiments and Analysismentioning

confidence: 99%

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Yuan

Zheng

2022

Mobile Information Systems

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“…When the sample image is input to the YOLOvl model, it contains five parameters, which are (x, y, h, w, and score), where (x, y) represents the center coordinates of the bounding box, h and w represent the height and width of the candidate box, respectively, and score represents the confidence [18]; the formula is as follows: score � Pr(Object) * IOU truth pred , (7) where Pr(Object) represents whether there is a detection target center point in the grid, and IOU represents the intersection ratio between the candidate frame A and the area B where the label target is located. e formula is as follows:…”

Section: Backpropagationmentioning

confidence: 99%

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Yang

2022

International Transactions on Electrical Energy Systems

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At present, the application of information technology has realized the transformation of people’s production and lifestyle, and it has also promoted the transformation of the sports industry. At present, the application of informatization in the basketball field in the sports industry is increasing. By using the advanced methods and technologies of its information display, this article aims at realizing the optimization of the basketball recognition pattern in the mobile network communication environment to promote the ecological development of the sports industry. It proposes to use panoramic vision to guide the integration of computer network and basketball field, which is helpful to analyze and solve the objectivity problems of single action and system incompatibility in basketball recognition simulation. By referring to the principles and laws of panoramic vision, the identification module and simulation module of the basketball auxiliary training system are constructed and optimized, so as to realize the promotion of basketball and the ecologicalization of teaching. In the research on the basketball recognition simulation system, the three-dimensional skeleton key point coordinate data are input into the ST-GCN network for comparison and testing. Specifically, it includes 2D key point coordinate data and 3D key point coordinate data as input to conduct model research and testing in the self-made basketball dataset. The experimental results show that the recognition rates of 2D and 3D coordinates are 66.64% and 87.69%, respectively. Therefore, it is crucial to use the human panoramic vision algorithm to convert 2D coordinates to 3D coordinates.

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“…In sport competitions, the body data of athletes are very important, especially gymnasts. Because the structure of human limbs is special, so there is no regularity in the process of movement, and the athletes' movement changes fast [1][2][3], which cannot be accurately identified by human eyes alone, resulting in the lack of posture identification of fitness gymnasts in the process of competition [4]. With the development of technology, some posture tracking recognition systems gradually applied in the fitness gymnastics competition and showed a certain effect.…”

Section: Introductionmentioning

confidence: 99%

Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data

Zhang

2021

Security and Communication Networks

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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.

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Research on basketball players’ action recognition based on interactive system and machine learning

Cited by 18 publications

References 19 publications

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data

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