Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

Author:

Tang Hongxin1ORCID

Affiliation:

1. Ministry of Sport and Military Affairs, China Ji Liang University, Hangzhou, Zhejiang 310018, China

Abstract

At present, the existing algorithm for detecting the parabola of tennis serves neglects the pre-estimation of the global motion information of tennis balls, which leads to great error and low recognition rate. Therefore, a new algorithm for detecting the parabola of tennis service based on video image analysis is proposed. The global motion information is estimated in advance, and the motion feature of the target is extracted. A tennis appearance model is established by sparse representation, and the data of high-resolution tennis flight appearance model are processed by data fusion technology to track the parabolic trajectory. Based on the analysis of the characteristics of the serve mechanics, according to the nonlinear transformation of the parabolic trajectory state vector, the parabolic trajectory starting point is determined, the parabolic trajectory is obtained, and the detection algorithm of the parabolic service is designed. Experimental results show that compared with the other two algorithms, the algorithm designed in this paper can recognize the trajectory of the parabola at different stages, and the detection accuracy of the parabola is higher in the three-dimensional space of the tennis service.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Reference19 articles.

1. Simulation of Tennis Match Scene Classification Algorithm Based on Adaptive Gaussian Mixture Model Parameter Estimation

2. Acute effects of a single tennis match on passive shoulder rotation range of motion, isometric strength and serve speed in professional tennis players

3. Tennis motion tracking based on particle filter in kalman filter prediction;R. Fu;Chinese Journal of Electron Devices,2019

4. Video multi-target detection technology based on recursive neural network;X. Hua;Application Research of Computers,2020

5. Small-scale moving target detection in aerial image by deep inverse reinforcement learning

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