Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

Computer Science > Artificial Intelligence [Submitted on 18 Mar 2026] Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering Antonius Bima Murti Wijaya, Paul Henderson, Marwa Mahmoud 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 propose and extensively evaluate a novel cluster-based approach that groups individuals based on similar attributes over time, enabling faster execution through accurate group summarisation. Our plug-and-play method can be combined with existing trajectory predictors by using our output centroid in place of their pedestrian input. We evaluate our proposed method on several challenging dense crowd scenes. We demonstrated that our approach leads to faster processing and lower memory usage when compared with state-of-the-art methods, while maintaining the accuracy Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.18166 [cs.AI]   (or arXiv:2603.18166v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.18166 Focus to learn more Submission history From: Antonius Bima Murti Wijaya [view email] [v1] Wed, 18 Mar 2026 18:04:54 UTC (14,506 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)