Motion capture and evaluation system of football special teaching in colleges and universities based on deep learning

Football reduces body fat and increases the tone of muscle, constructs strength, flexibility, and stamina. It increases muscle power, bone as well as improvements in walking, running, and jumping.The challenging characteristics in motion capture of football teaching include lack of latency, complexity, and physical interaction analysis of sports performance. In this paper, Deep learning assisted motion capture system has been proposed to enhance bandwidth, the variability of performance, and realistic physical interactions in an accurate manner in colleges and universities. Bidirectional motion analysis is implemented to reduce animation costs and enhance motion capturing data in football events. Network evaluation management technique is introduced to recreate the intricate and realistic physical interactions of unique football teaching colleges and universities. The simulation analysis is performed based on complexity; performance, latency, and efficiency prove the proposed framework's reliability.