Abstract
To solve the problems of low accuracy and high time cost in manual recording and statistics of basketball data, an automatic analysis method of motion action under the basketball sports scene based on the spatial temporal graph convolutional neural network is proposed. By using the graph structure in the data structure to model the joints and limbs of the human body, and using the spatial temporal graph structure to model the posture action, the extraction and estimation of human body posture in basketball sports scenes are realized. Then, training combined with transfer learning, the recognition of motion fuzzy posture is realized through the classification and application of a label subset. Finally, using the self-made OpenCV to collect and calibrate NBA basketball videos, the effectiveness of the proposed method is verified by analyzing the motion action. The results show that the proposed method based on the spatial temporal graph convolutional neural network can recognize all kinds of movements in different basketball scenes. The average recognition accuracy is more than 75%. It can be seen that the method has certain practical application value. Compared with the common motion analysis method feature descriptors, the motion action analysis method based on the spatial temporal graph convolution neural network has higher identification accuracy and can be used for motion action analysis in the actual basketball sports scenes.
Highlights
Introduction of Spatial Temporal Graph ConvolutionalNeural Networks e spatial temporal graph convolution neural network redefines convolution according to the graph structure, and it enables the graph structure to perform convolution operations
The boundary of estimating motion in basketball is relatively fuzzy, which increases the difficulty of research [13–15]. erefore, in order to solve the above problems, on the basis of the existing research, utilizing the powerful learning ability of the spatial temporal graph convolutional network (STGCN), this study proposes a method of analysis of motion action in basketball sports scene based on image processing and spatial temporal convolutional neural network
Introduction of Spatial Temporal Graph Convolutional Neural Networks e spatial temporal graph convolution neural network redefines convolution according to the graph structure, and it enables the graph structure to perform convolution operations
Summary
Basketball is one of the most popular sports competitions. e analysis of motion action in the basketball sports scene is helpful to improve basketball players’ skills. Erefore, in order to solve the above problems, on the basis of the existing research, utilizing the powerful learning ability of the spatial temporal graph convolutional network (STGCN), this study proposes a method of analysis of motion action in basketball sports scene based on image processing and spatial temporal convolutional neural network. Γ is the parameter, representing the time length of the spatial temporal convolution kernel It is responsible for setting the distance threshold of adjacent nodes added into the subset to less than Γ/2 from vti in the time axle distance. E weight function is for the root node vti, and the label mapping lST(vqj) of adjacent node set of the spatial temporal graph structure can be expressed as lSTvqi ltivtj +q − t +Γ2 × K. Basketball Motion Analysis Method Based on Spatial Temporal Graph Convolutional Network
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