Automated people counting at a mass site

Abstract

Reliable estimation of people in public areas is an important problem in visual surveillance. Although there is a lot of research on people counting in recent years, most of them consider a small crowd of people without many serious occlusions. Some of them have a lot of particular requirements, like people are moving, the background is smooth or the image resolution is high. This paper aims to estimate the number of people in a complicated scenario, which has around one hundred persons in an outdoors event. Several people counting methods based on crowd density are considered to find the relationship between the foreground pixels and the number of people in the large crowd. The best estimation result is from the method that considers two types of foreground pixels: those that come from relatively stationary crowd, and those that come from moving people. In an evaluation of three developed methods over 51 cases, the best average error is around 10%. All the proposed methods do not have any special requirements on the resolution of the input video. © 2008 IEEE.