Ensembled Pretrained Model-Based Adaptive Sequential Trace Recognition for Moving Objects.

In this paper, we propose an adaptive continuous trajectory recognition method for badminton sports scenarios. First, we adopt an integrated pre-training model with high generality and expressive ability as the infrastructure. By pre-training large-scale badminton game data, the model can learn the features of badminton sports from rich and diverse trajectory data. Second, to adapt to the trajectory differences in different scenarios, we introduce an adaptive mechanism. By extracting and encoding features from real-time acquired badminton motion trajectories, the model can adjust its own parameters and weight assignments according to the changes in the current scene to optimally recognize and track the motion targets. The algorithm also employs the Kalman filter aggregation Enhanced Correlation Coefficient (ECC) method of the motion model to improve the prediction accuracy. Finally, a series of experiments and comparisons are conducted to validate the effectiveness of the approach. The results show that our proposed adaptive continuous trajectory recognition method for badminton achieves better performance in different scenarios and various complexities, and has higher accuracy and robustness than the traditional method. The FDA-SSD model operates at 28.7 fps, which is about 16.5% faster than the traditional SSD. In the target tracking experiments, the target tracking mean squared error is about 1.0%. In the target tracking experiments based on the FDA and geometrically constrained localization method, the root-mean-square error of the target tracking is less than 4.72 cm. [ABSTRACT FROM AUTHOR]