Spatial Variability Optimization Of Volley Ball Player’s Position Rotation In Machine Vision

Abstract

Under the computer machine vision, when the traditional method is used to study the spatial characteristics of volleyball player’s position rotation, with the increasing uncertainty of volleyball player’s position rotation, the spatial state recognition effect is not good, and the guidance for adjusting tactics is not strong. In this paper, a design method of 3D gesture reconstruction system for volleyball spiking based on stereo vision was proposed. First of all, the 2D gesture image of volleyball spiking was obtained by sensors or cameras, and then a smooth denoising was conducted to the noise. Secondly, based on the calibration principle of 2D plane target, a 3D gesture image geometric model of volleyball spiking was established and its parameters were obtained.Finally, binocular stereo vision algorithm was used to realize the feature extraction and matching of the 3D dimensional gesture image of the volleyball spiking action and complete the 3D gesture reconstruction.By solving the action parameter equation of the visual feature image of the volleyball player’s position rotation, the spatial variability of the position rotation was optimized.The experimental results showed that the algorithm could accurately and effectively analyze and identify the spatial characteristics of the volleyball player’s position rotation under machine vision, and it had an important guiding significance for improving the players’coordination and cooperation quality.