An Anticipative Crowd Management System Preventing Clogging in Exits During Pedestrian Evacuation Processes

This paper presents an anticipative system which operates during pedestrian evacuation processes and prevents escape points from congestion. The processing framework of the system includes four discrete stages: a) the detection and tracking of pedestrians, b) the estimation of possible route for the very near future, indicating possible congestion in exits, c) the proposal of free and nearby escape alternatives, and d) the activation of guiding signals, sound and optical. Detection and tracking of pedestrians is based on an enhanced implementation of a system proposed by Viola, Jones, and Snow that incorporates both appearance and motion information in near real-time. At any moment, detected pedestrians can instantly be defined as the initial condition of the second stage of the system, i.e., the route estimation model. Route estimation is enabled by a dynamic model inspired by electrostatic-induced potential fields. The model combines electrostatic-induced potential fields to incorporate flexibility in the movement of pedestrians. It is based on Cellular Automata (CA), thus taking advantage of their inherent ability to represent effectively phenomena of arbitrary complexity. Presumable congestion during crowd egress, leads to the prompt activation of sound and optical signals that guide pedestrians towards alternative escaping points. Anticipative crowd management has not been thoroughly employed and this system aims at constituting an effective proposal.