A novel method for crowd density estimations
Haiyan Yang, Hua An Zhao · 2012
Crowd density estimation is important in crowd analysis; this paper proposes a new approach used for crowd density estimation. First, background is removed by using a combination of optical flow and background subtracts methods. Then according to texture analysis, a set of new feature is extracted from foreground image. Finally, a self-organizing map neural network is used for classifying different crowds. Some experimental results show compared to former crowd estimation methods, the proposed approach can carry out estimation more accurately; the rate of true classification is 85.6% on a data set of 500 images.