Journal article

Multi-view crowd congestion monitoring system based on an ensemble of convolutional neural network classifiers

Yan Li, Majid Sarvi, Kourosh Khoshelham, Milad Haghani

Journal of Intelligent Transportation Systems | Taylor & Francis Inc. | Published : 2020


Multi-view video surveillance is a highly valuable tool to ensure the safety of the crowd in large public space. By utilizing complementary information captured by multiple cameras, the issue of limited views and occlusion in single views can be addressed to gain better insight into the whole monitored space. However, multi-view surveillance has been widely applied to microscopic crowd analysis, for example pedestrian detection and tracking, while macroscopic level analysis, which deals with the whole crowd, has received little attention. We propose a multi-view framework for the generation of level of service maps, which are the most commonly used measure of congestion at macroscopic level,..

View full abstract