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

Video-based Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes