Automatic detection of dangerous motion behavior in human crowds
Abstract
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.
