Correlated Motion Based Crowd Analysis in Queueing Situations

Abstract

Crowd analysis by automated visual surveillance represents a challenging task in many practically relevant scenarios. In this paper we address the problem of capturing relevant correlated movement within a line formed by waiting pedestrians to estimate the time needed for the last person to reach the queue front. To obtain a waiting time estimate we propose to solve two interlinked problems: queue shape delineation and motion characterization estimating the propagation velocity along the segmented queue. Accordingly, we present a scheme to reliably segment the queue shape by finding and refining an optimum path over time. The optimality condition refers to minimizing its length while maximizing its overlap with observed correlated motion patterns. To capture the collective motion of the crowd within the queue we employ a deformable chain structure to temporally aggregate the relevant short-term forward movement by tracking. The resulting tracked chain structure is used to generate a mean forward propagation velocity estimate. The presented approach represents a general analysis scheme, requiring only a set of tracked pedestrians on a calibrated ground plane at every frame. We validate our proposed scheme on two real datasets with time-varying queue structures. Based on a comparison to manually-set ground truth, obtained results show that queue delineation and waiting time estimates are reliable, can cope with motion clutter and well characterize the waiting behavior and its temporal evolution.