A recognition method of basketball players' shooting action based on Gaussian mapping

被引:0
作者
Xia Z. [1 ]
机构
[1] The Engineering and Technical College of Chengdu University of Technology, Leshan
关键词
action recognition; basketball; feature extraction; Gaussian mapping; shooting action;
D O I
10.1504/IJRIS.2023.130192
中图分类号
学科分类号
摘要
In order to overcome the problem of low recognition accuracy of traditional action recognition methods, this paper proposes a basketball player shooting action recognition method based on Gaussian mapping. Firstly, the basketball shooting image is preprocessed by block initialisation and denoising to improve the quality of the image. Secondly, based on the image preprocessing results, Gaussian mapping is used to extract the target features of shooting action image. Finally, according to the target characteristics, the multi-level feature decomposition and fuzzy processing of the image are carried out to realise the shooting action recognition. Experiments show that the designed method has high accuracy and recall rate, the maximum recognition accuracy reaches 96%, and the recognition time is short, and the number of false recognition frames is less, which shows that the designed method has high practical application performance. Copyright © 2023 Inderscience Enterprises Ltd.
引用
收藏
页码:105 / 110
页数:5
相关论文
共 18 条
[1]  
Deepakraj D., Raja K., Markov-chain based optimization algorithm for efficient routing in wireless sensor networks, International Journal of Information Technology, 13, 3, pp. 897-904, (2021)
[2]  
Fan J., Bi S., Wang G., Zhang L., Sun S., Sensor fusion basketball shooting posture recognition system based on CNN, Journal of Sensors, 21, 12, pp. 1-16, (2021)
[3]  
Guimares E., Baxter-Jones A., Williams A.M., Tavares F., Janeira M.A., Maia J., Modelling the dynamics of change in the technical skills of young basketball players: the INEX study, PLoS ONE, 16, 9, pp. 257-267, (2021)
[4]  
Ji C., Wang C., Image sequence weak and small target recognition based on multi-modal deep learning, Computer Simulation, 39, 2, pp. 414-418, (2022)
[5]  
Ji R., Research on basketball shooting action based on image feature extraction and machine learning, IEEE Access, 8, 2, pp. 12-16, (2020)
[6]  
Junjun G., Basketball action recognition based on FPGA and particle image, Microprocessors and Microsystems, 80, 14, pp. 1033-1039, (2021)
[7]  
Kostkova J., Flusser J., Lebl M., Pedone M., Handling Gaussian blur without deconvolution, Pattern Recognition, 10, 3, pp. 107-114, (2020)
[8]  
Kumar R., Kumar N., Jung K.H., Color image steganography scheme using gray invariant in AMBTC compression domain, Multidimensional Systems and Signal Processing, 31, 3, pp. 1145-1162, (2020)
[9]  
Li B., Wang X., Yao J., Application algorithms for basketball training based on Big Data and Internet of Things, Mobile Information Systems, 31, 1, pp. 1-8, (2021)
[10]  
Liu N., Liu P., Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things, The Journal of Supercomputing, 16, 2, pp. 1-21, (2021)