2011
,
Krausz, Barbara
,
Bauckhage, Christian
Tragically, mass gatherings such as music festivals, sports events or pilgrimage quite often end in terrible crowd disasters with many victims. In the past, research focused on developing physical models that model human behavior in order to simulate pedestrian flows and to identify potentially hazardous locations. However, no automatic systems for detection of dangerous motion behavior in crowds exist. In this paper, we present an automatic system for the detection and early warning of dangerous situations during mass events. It is based on optical flow computations and detects patterns of crowd motion that are characteristic for hazardous congestions. By applying an online change-point detection algorithm, the system is capable of identifying changes in pedestrian flow and thus alarms security personnel to take necessary actions.
2011
,
Krausz, Barbara
,
Bauckhage, Christian
Congestions in pedestrian traffic typically occur when the number of pedestrians exceeds the capacity of pedestrian facilities. In some cases, the pedestrian density reaches a critical level which may lead to a crowd stampede as happens rather frequently at mass gatherings, in stadiums or at train stations. In the past, research has focused on improving simulations of crowd motion in order to identify potentially dangerous locations and to direct pedestrian streams. Recently, works towards the automatic real-time detection of critical mass behavior based on optical flow computations have been proposed. In this paper, we verify these approaches by analyzing mircoscopic pedestrian behavior in congestions and conducting experiments on synthetic as well as on real datasets.