Visualization Analysis of Integrating College Sports Training and Psychology into Basketball Physical Education Teaching System Based on Image Recognition Algorithm

• Published in: Applied artificial intelligence Vol. 38; no. 1
• Main Authors: Cheng, Junzhang, Wen, Lingtao, Zhang, Baolei
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Back propagation networks; Basketball; Feature extraction; Neural networks; Psychology; Visualization
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2024.2419571

On this basis, a basketball movement model based on ORB (Oriented FAST and Rotated BRIEF) local feature extraction has been proposed, and a basketball teaching visualization analysis system has been constructed. By extracting images from athletes' videos in basketball, and using their training as a standard, tracking the frequency of their various celebration postures appearing and improving their scores in the game, corresponding feature points are extracted, and then the BP (back propagation) neural network algorithm is used to classify feature points. Ten different participants were selected from 10 games. Their movements were tracked, and then the extracted feature actions were imported into the visualization system for visual analysis, comparing the changes before and after psychological intervention. The results showed that there were significant differences in scores, errors, hit rates, steals, and motivation among subjects who received psychological cues and incentives compared to the data before psychological counseling. The scores and positive and negative values increased to 2-4 points, which had a certain positive impact on the competitive results.