An Algorithm for Extracting Features of Basketball Players' Foul Actions Based on an Attention Mechanism

• Published in: International journal of computational intelligence systems Vol. 18; no. 1; pp. 1 - 15
• Main Author: Wang, Peng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 24.02.2025; Springer Nature B.V; Springer
• Subjects: Algorithms; Artificial Intelligence; Basketball; Computational Intelligence; Control; Engineering; Entropy (Information theory); Error detection; Feature extraction; Foul action; Machine vision; Machine vision system; Mathematical Logic and Foundations; Mechatronics; Modules; Players; Real time; Research Article; Robotics; Spatial attention; Temporal attention; Vision systems; Machine vision system; Temporal attention; Feature extraction; Foul action; Spatial attention
• ISSN: 1875-6883; 1875-6891; 1875-6883
• DOI: 10.1007/s44196-025-00743-5

Basketball foul action usually involves multiple features, and it is difficult to extract effective features from complex and diverse features. Therefore, a feature extraction algorithm for basketball players' foul action based on attention mechanism is proposed. On the basis of ResNet50 network model, the attention mechanism is integrated to build a basketball player foul action feature extraction model. The machine vision system is used to obtain basketball player action video as the input of the model. The space attention module and time attention module are, respectively, introduced into the video slow frame rate branch and video fast frame rate branch of ResNet50 network. By making the network give greater weight to the key areas of a single frame of video, and improving the network's attention to important video frames, the best spatio-temporal characteristics of foul actions are obtained. After fusion, the feature fusion results are input into the classifier with the cross-entropy loss function as the loss function, and the probability value of each possible foul action and the tag target probability are output, complete basketball player foul action feature extraction. The experimental results show that the algorithm can effectively identify the identification of basketball players, the cumulative matching feature value of foul action recognition can reach more than 95%, and the average accuracy is more than 70%; the F 1 value and stability are high, which can reduce the error caused by data fluctuation and noise; the error rate of real-time detection is less than 4.5%, the omission rate is less than 4.7%, the detection time is lower than 14 ms, and the application effect is good.