  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.
