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

Abstract

AbstractTo 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.