Designing a Basketball Action Recognition System Based on the Improved OpenPose Algorithm - Su, Zhenhai (2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 2024)
In the realm of computer-assisted learning, video tutorials have gained significant traction for teaching various sports, including basketball. However, these tutorials often suffer from issues such as lack of standardization, clarity, and effectiveness. To address these challenges, this study presents a novel approach: a basketball action recognition system built upon an enhanced OpenPose algorithm. This system efficiently processes videos or images captured by learners, identifies crucial action frames, reconstructs human skeletal structures, computes joint angles, compares them with standard actions, and offers real-time posture correction guidance. Experimental validation showcases the system's potential as a valuable tool for learning and honing basketball techniques. Furthermore, its adaptability suggests broader applications across diverse sports education contexts, emphasizing its practical significance and potential impact. The integration of machine learning algorithms ensures continuous improvement and customization, catering to individual learning needs and enhancing overall learning experiences.