Trajectory-BERT: Pre-training and fine-tuning bidirectional transformers for crowd trajectory enhancement

To address the issue of trajectory fragments and ID switches caused by occlusion in dense crowds, we propose a space-time trajectory encoding method and a point-line-group division method to construct Trajectory-BERT in this paper. Leveraging the spatiotemporal context-dependent features of trajectories, we introduce pre-training and fine-tuning Trajectory-BERT tasks to repair occluded trajectories. Experimental results show that data augmented with Trajectory-BERT outperforms raw annotated data on the MOTA metric and reduces ID switches in raw labeled data, demonstrating the feasibility of our method.