Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering

• URL: http://arxiv.org/abs/2603.18166v1
• Date: Wed, 18 Mar 2026 18:04:54 GMT
• Title: Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
• Abstract summary: Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes.<n>Recent works address the problem by predicting individual trajectories and considering surrounding objects based on manually annotated data.<n>We propose and extensively evaluate a novel cluster-based approach that groups individuals based on similar attributes over time.
• Score: 4.663888628491985
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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

Related papers

• Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling [54.94692733670454]
  Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles.<n>We propose a lightweight yet highly accurate streaming-based trajectory forecasting approach.<n>Our approach significantly reduces inference latency, making it well-suited for real-world deployment.
  arXiv  Detail & Related papers  (2026-03-02T13:44:23Z)
• Don't double it: Efficient Agent Prediction in Occlusions [1.1338062042380708]
  Occluded traffic agents pose a significant challenge for autonomous vehicles, as hidden pedestrians or vehicles can appear unexpectedly.<n>Existing learning-based methods often produce redundant occupancy predictions where a single agent is identified multiple times.<n>We introduce MatchInformer, a novel transformer-based approach that builds on the state-of-the-art SceneInformer architecture.
  arXiv  Detail & Related papers  (2026-01-29T10:22:38Z)
• Density Estimation and Crowd Counting [3.9573534520147806]
  The proposed method integrates a denoising probabilistic model that utilizes diffusion processes to generate high-quality crowd density maps.<n>A regression branch is incorporated into the model for precise feature extraction, while a consolidation mechanism combines these maps based on similarity scores to produce a robust final result.<n>An event-driven sampling technique, utilizing the Farneback optical flow algorithm, is introduced to selectively capture frames showing significant crowd movements.
  arXiv  Detail & Related papers  (2025-11-12T20:30:45Z)
• ProgRoCC: A Progressive Approach to Rough Crowd Counting [66.09510514180593]
  We label Rough Crowd Counting that delivers better accuracy on the basis of training data that is easier to acquire.<n>We propose an approach to the rough crowd counting problem based on CLIP, termed ProgRoCC.<n>Specifically, we introduce a progressive estimation learning strategy that determines the object count through a coarse-to-fine approach.
  arXiv  Detail & Related papers  (2025-04-18T01:57:42Z)
• ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking [41.889032460337226]
  ProTracker is a novel framework for accurate and robust long-term dense tracking of arbitrary points in videos.<n>This design effectively combines global semantic information with temporally aware low-level features.<n>Experiments demonstrate that ProTracker attains state-of-the-art performance among optimization-based approaches.
  arXiv  Detail & Related papers  (2025-01-06T18:55:52Z)
• Pedestrian Motion Prediction Using Transformer-based Behavior Clustering and Data-Driven Reachability Analysis [1.8963583458350768]
  We present a transformer-based framework for predicting future pedestrian states based on historical trajectory data. We utilize a transformer encoder coupled with hierarchical density-based clustering to automatically identify diverse behavior patterns. We show that these behavior clusters can be used with data-driven reachability analysis, yielding an end-to-end data-driven approach to predicting the future motion of pedestrians.
  arXiv  Detail & Related papers  (2024-08-09T07:24:30Z)
• Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations [53.797896854533384]
  Class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming. We introduce three simple spatial and temporal regularization losses, which facilitate the self-supervised training process effectively.
  arXiv  Detail & Related papers  (2024-03-20T02:58:45Z)
• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference [54.17205151960878]
  We introduce a sampling-free approach that is generic and easy to deploy. We produce reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost.
  arXiv  Detail & Related papers  (2022-11-21T13:23:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.