3D crowd surveillance and analysis using laser range scanners

In this study, we present a novel system for crowd surveillance and quantified analysis based on laser range scanners. By mounting a laser scanner at a swinging platform, the spatial information of passengers inside the area of interest can be reconstructed in a form of 3D points. Multiple laser scanners are integrated together by semi-auto calibration procedures. Background map is generated through histogram analysis of scan maps, and is further applied for 3D moving object detection. An improved version of mean-shift clustering algorithm is proposed to extract individual passengers efficiently. In addition, quantified crowdness analysis is conducted from different aspects to indicate the situation inside the surveillance area according to the extraction results of passengers. The proposed system was tested in a central subway station in Tokyo and experimental results demonstrate the effectiveness of our proposed system.