Bottleneck Detection in Crowded Video Scenes Utilizing Lagrangian Motion Analysis Via Density and Arc Length Measures

Abstract

Bottleneck situations can occur in overcrowded areas such as entrances or narrowed passages and are associated with a great danger to the life and health of involved people. The automated detection of such bottlenecks is the first crucial step to mitigate these dangers. In this work, we utilize the dynamics of motions using the Lagrangian approach from the analysis of dynamic systems to analyze profiles of groups of people. The derived features, which are observed by the long-term dependent motion dynamics, are described by two-dimensional Lagrangian fields. We extend the underlying Lagrangian framework by a novel measure to capture the density of motion and hence people in the context of crowd analysis. Further, we show how this novel density measure can be combined with the established arc length measure for the detection of bottlenecks in videos. Experimental evaluations show a 5% improvement over the state-of-the-art for spatiotemporal bottleneck detection.