Multi-view crowd congestion map generation based on ensemble learning

Abstract

Multi-view video surveillance has been a major research area in crowd congestion management. By exploiting complementary information captured by multiple cameras, the limited views and occlusion in single views can be addressed to gain an insight into the whole monitored space. However, multi-view surveillance has been widely applied to microscopic crowds analysis, for example pedestrian detection and tracking, while macroscopic level analysis, which deals with the whole crowd, has received little attention. Level of service (LOS) is the most widely accepted standard of measuring congestion at macroscopic level and level of service maps are the most straightforward way of showing distributio..