CrowdTraj: A Benchmark for Dense Crowd Trajectory Prediction in Realistic Crowded Environments

2026-09-07

AI Summary

针对密集人群轨迹预测问题，提出CrowdTraj基准，支持从检测到跟踪再到轨迹预测的端到端评估，适用于严重遮挡的CCTV视角。

Abstract

In real-world applications, pedestrian trajectory prediction models rely on inputs from detection and tracking systems. Prior trajectory prediction benchmarks either contain relatively sparse pedestrian interactions, assume perfect tracking inputs, or rely on overhead viewpoints that minimize occlusion and perspective distortion, limiting evaluation in realistic dense-crowd scenarios. We present CrowdTraj, a benchmark for pedestrian trajectory prediction in natural dense crowd scenes. Unlike previous datasets, CrowdTraj supports end-to-end evaluation from detection through tracking to trajectory prediction under severe occlusion in CCTV views. It also captures diverse, natural pedestrian behaviours, including abrupt directional changes rarely observed in existing benchmarks. CrowdTraj includes five diverse scenes, with an average of 1,146 unique pedestrians per scene, maximum frame-level densities ranging from 114 to 372 pedestrians, and over 3.2 million annotated head bounding boxes. CrowdTraj provides pixel and real-world coordinates via per-scene homography matrices for physically meaningful analysis. Our experimental results show that tracking accuracy (IDF1) drops to 0.68 to 0.70 in the densest scenes, compared with approximately 0.90 in less crowded scenes. Trajectory prediction training also becomes substantially more computationally expensive in dense scenes, with training times increasing by up to 8 times. These findings show that CrowdTraj exposes limitations in current trajectory prediction pipelines that remain hidden on existing sparse-crowd benchmarks, particularly in robustness to tracking noise and computational scalability.

Problem

• Dense Crowd
• Trajectory Prediction
• Realistic Environments
• Pedestrian Interaction
• Occlusion

Innovation

• Dense Crowd Trajectory Prediction
• Realistic Environments
• End-to-End Evaluation
• Severe Occlusion
• Tracking Noise Robustness