Pedestrian Counting Based on Crowd Density Estimation and Lucas-Kanade Optical Flow
This paper proposes a novel method to estimate crowd densities in regions and count pedestrians passing through a virtual line. Firstly, the crowd density estimation for regions is based on regional feature analysis and support vector regression (SVR). We extract the following features from each segmented region: the pixel ratio and block-size histogram of the foreground, the pixel ratio and the Minkowski dimension of the edge image, and the gray-level co-occurrence matrix (GLCM) features of the gray image. SVR is used to train and estimate the crowd densities of regions with the extracted features. Secondly, we use the estimated crowd densities and the Lucas-Kanade (LK) optical flow to count pedestrians passing through the line. In each region, we divide the estimated crowd density by the number of foreground pixels and get the density per foreground pixel. Then, people moving speeds based on the LK optical flow are used to compute the number of foreground pixels crossing the line. After that, we can estimate the number of people passing through the line by multiplying the density per foreground pixel by the number of foreground pixels crossing the line. Each region is segmented into several small blocks for higher accuracy to compute the people moving speeds. The experimental results show that the proposed method has a good performance on both crowd density estimation and pedestrian counting.