This article describes a video-surveillance system developed within the ISCAPS project. Thermal imaging provides a robust solution to visibility change (illumination, smoke) and is a relevant technology for discriminating humans in complex scenes. In this article, we demonstrate its efficiency for posture analysis in dense groups of people. The objective is to automatically detect several persons lying down in a very crowded area. The presented method is based on the detection and segmentation of individuals within groups of people using a combination of several weak classifiers. The classification of extracted silhouettes enables to detect abnormal situations. This approach was successfully applied to the detection of terrorist gas attacks on railway platform and experimentally validated in the project. Some of the results are presented here.
['Quoc-Cuong Pham', 'Laetitia Gond', 'Julien Begard', 'Nicolas Allezard', 'Patrick Sayd']
Real-Time Posture Analysis in a Crowd using Thermal Imaging