Visualization of movements in sports training based on multimedia information processing technology

The rapid development of multimedia information processing technology provides development opportunities for digitization in sports, among which motion capture technology, as the latest achievement of multimedia information processing technology, has gradually gained the attention of scholars and started to be used for visualization of sports movements. Therefore, this paper introduces a monocular video motion capture method and optimizes it for the problems of reconstructing human movements such as floating, ground penetration and sliding, which provides a technical path for the specific application of motion capture technology in the field of sports training and also provides a technical guarantee for the visualization of sports training movements. Introduced a new motion capture optimization method. This method captures human motion trajectories from monocular videos, and trajectory operations combine human pose estimation and physical constraints. The proposed method uses foot contact judgment to obtain foot contact events for each motion frame. Then, it optimizes the overall body motion trajectory of the key points based on the obtained contact conditions, making the generated motion visually closer to reality. This article proposes LiteHumanPose Net with a inference speed of up to 22FPS, and conducts experimental analysis and comparison of several popular pose estimation methods from the perspectives of frame rate and average accuracy, such as Sim pleBaseline, HRNet, and Hourglass Net. LiteHumanPose Net outperforms Hourglass Net in terms of frame rate and accuracy, while HRNet has high accuracy due to its multiple parameters but low frame rate. The LiteHumanPose network proposed in this article has a good balance between accuracy and frame rate, and has obvious landing advantages.