Video-based Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes

TL;DR

This paper introduces a novel video-based method for detecting crowd bottlenecks by analyzing Lagrangian motion patterns and FTLE fields, enabling automatic spatio-temporal identification of congestion points.

Contribution

The work presents a new Lagrangian dynamics framework utilizing FTLE fields for global crowd segmentation and bottleneck detection in videos, validated on updated datasets.

Findings

Effective detection of bottlenecks in crowded scenes

Robust segmentation of crowd movements over time

Validated approach on real-world datasets

Abstract

Avoiding bottleneck situations in crowds is critical for the safety and comfort of people at large events or in public transportation. Based on the work of Lagrangian motion analysis we propose a novel video-based bottleneckdetector by identifying characteristic stowage patterns in crowd-movements captured by optical flow fields. The Lagrangian framework allows to assess complex timedependent crowd-motion dynamics at large temporal scales near the bottleneck by two dimensional Lagrangian fields. In particular we propose long-term temporal filtered Finite Time Lyapunov Exponents (FTLE) fields that provide towards a more global segmentation of the crowd movements and allows to capture its deformations when a crowd is passing a bottleneck. Finally, these deformations are used for an automatic spatio-temporal detection of such situations. The performance of the proposed approach is shown in extensive evaluations on the existing J\"ulich and AGORASET datasets, that we have updated with ground truth data for spatio-temporal bottleneck analysis.