Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

ArXi:2603.18166v1 Announce Type: new Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works address the problem by predicting individual trajectories and considering surrounding objects based on manually annotated data. However, these approaches tend to overlook dense crowd scenarios, where the challenges of automation become pronounced due to the massiveness, noisiness, and inaccuracy of the tracking outputs, resulting in high computational costs.