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.

中文翻译：

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Trajectory-BERT：用于增强人群轨迹的预训练和微调双向变换器

针对密集人群中遮挡造成的轨迹碎片和ID切换问题，本文提出了一种时空轨迹编码方法和点线组划分方法来构建Trajectory-BERT。利用轨迹的时空上下文相关特征，我们引入了预训练和微调 Trajectory-BERT 任务来修复被遮挡的轨迹。实验结果表明，使用 Trajectory-BERT 增强的数据在 MOTA 指标上优于原始注释数据，并减少了原始标记数据中的 ID 开关，证明了我们方法的可行性。