A Diffusion Neural Network-Enhanced Object Tracking Approach Under Sports Scenarios

Object tracking for motion scenes is a common research concern in field of computer vision. Its goal is to accurately track targets in different time periods and predict their future states by utilizing the motion information in video sequences. However, traditional target-tracking methods in motion scenes often face challenges such as target blur, occlusion, and changes in lighting. To deal with this issue, this paper proposes a diffusion neural network-enhanced object-tracking approach under sports scenarios. In order to further improve tracking performance, the diffusion convolution operation is introduced, which propagates features at different time steps to enhance the modeling ability of target motion. Then, suitable influencing factors are selected based on motion scene object feature parameters. Finally, a target tracking method is established by integrating these two methods. In the experiment, we used a large number of real motion scene datasets to evaluate the proposed method. The experimental results show that by comparing with traditional moving object tracking methods, the proposal achieves significant improvement in tracking accuracy and methodology robustness. In addition, we also conducted stability experiments, proving that this method has good stability for models with varying kernel numbers.