Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
arXiv:2603.18166v1 Announce Type: new 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 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 sce
arXiv:2603.18166v1 Announce Type: new 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 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
Executive Summary
This article proposes a novel cluster-based approach to dense crowd trajectory prediction, addressing the challenges of automation in dense crowd scenarios. The method groups individuals based on similar attributes over time, enabling faster execution and accurate group summarisation. The proposed approach is evaluated on several challenging dense crowd scenes, demonstrating faster processing, lower memory usage, and comparable accuracy to state-of-the-art methods. This approach has the potential to improve public safety and management by enabling real-time crowd trajectory prediction in dense crowd scenarios.
Key Points
- ▸ The article proposes a novel cluster-based approach to dense crowd trajectory prediction.
- ▸ The method groups individuals based on similar attributes over time, enabling faster execution.
- ▸ The approach is evaluated on several challenging dense crowd scenes, demonstrating improved performance.
Merits
Strength in Addressing Dense Crowd Scenarios
The proposed approach addresses the challenges of automation in dense crowd scenarios, where massiveness, noisiness, and inaccuracy of tracking outputs are pronounced, leading to high computational costs.
Improved Performance and Efficiency
The method demonstrates faster processing and lower memory usage compared to state-of-the-art methods, while maintaining comparable accuracy.
Demerits
Limited Evaluation on Real-World Data
The article evaluates the proposed approach on several challenging dense crowd scenes, but it is unclear whether the results are generalizable to real-world data and scenarios.
Dependence on Pre-existing Trajectory Predictors
The proposed approach requires the use of existing trajectory predictors, which may limit its applicability and flexibility in real-world applications.
Expert Commentary
This article presents a novel and promising approach to dense crowd trajectory prediction, addressing the challenges of automation in dense crowd scenarios. The proposed method demonstrates improved performance and efficiency compared to state-of-the-art methods, while maintaining comparable accuracy. However, the article's limitations, such as limited evaluation on real-world data and dependence on pre-existing trajectory predictors, need to be addressed in future research. Furthermore, the article's implications for public safety and management policies are significant, and the proposed approach has the potential to improve crowd management in various real-world applications.
Recommendations
- ✓ Future research should evaluate the proposed approach on real-world data and scenarios to ensure its generalizability and applicability.
- ✓ The proposed approach should be further developed and refined to reduce its dependence on pre-existing trajectory predictors.
