Estimation of crowd density using multi-local features and regression

Crowd analysis is an important issue in intelligent visual surveillance systems. In this paper, a tracking-free solution to crowd density estimation is presented. The method consists of four steps: each motion parts are first extracted from video frames through motion segmentation; then eight kinds of low-level image features including blob area, Harris corner, KLT feature points, contour number, contour perimeter, ratio of perimeter to area, edge and fractal dimension are calculated; to eliminate the errors introduced by perspective effect and occlusion, both geometric correction and overlapping compensation through proper weight assignments are performed; finally, multiple regression model is used to estimate pedestrian numbers. Various experiments are performed on three video data sets and the encouraging results show that the proposed algorithm not only can perform crowd density estimation correctly but can operate in real-time.