arXiv Artificial Intelligence

Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

2 hours ago 09.10.2026 07:00 👁 2 views
Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

Quick summary

arXiv:2603.18166v2 Announce Type: replace Abstract: 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 more pronounced due to the massiveness, noisiness, and inaccuracy of the tracking outputs, resulting in high computational costs. To address these challenges, we

Key takeaways

  • arXiv:2603.18166v2 Announce Type: replace Abstract: 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 more pronounced due to the massiveness, noisiness, and inaccuracy of the tracking outputs, resulting in high computational costs.

Why it matters

“Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗