Motion Action Analysis at Basketball Sports Scene Based on Image Processing

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.

Athletes' Action Recognition and Posture Estimation Algorithm Based on Image Processing Technology

“…This algorithm harnesses the power of computer vision techniques to analyze video footage or images of athletes in action, allowing for real-time assessment of their movements and postures [7]. The algorithm employs sophisticated machine learning models trained on vast datasets of annotated sports activities to accurately recognize and classify various actions performed by athletes [8]. Whether it's a basketball player shooting a three-pointer or a soccer player executing a bicycle kick, the algorithm can identify these actions with high precision.…”

Study on the Segmentation Method of the Improved DeepLabv3+ Algorithm in the Basketball Scene

“…At present, the identi cation method of athlete attributes still studies the existence of attributes without obtaining the position information of the attributes of man and the ball. Obtaining the position of the player and the ball, that is, accurate positioning, is the premise of attribute judgment, which is very meaningful for semantic segmentation of basketball scenes [3].…”