标题：Crowd Density Estimation: An Improved Approach
作者：Li, Wei; Wu, Xiaojuan; Matsumoto, Koichi; Zhao, Hua-An
作者机构：[Li, Wei; Wu, Xiaojuan] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Matsumoto, Koichi; Zhao, Hua-An] Kumamoto Univ, 更多
会议名称：IEEE 10th International Conference on Signal Processing
会议日期：OCT 24-28, 2010
来源：2010 IEEE 10TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS (ICSP2010), VOLS I-III
关键词：feature extraction and analysis; moving object detection; scene; analysis; crowd density estimation
摘要：Crowd density estimation is important in crowd analysis and texture analysis is an efficient method to estimate crowd density, this paper proposes an improved estimation approach based on texture analysis. First, background is removed by using a combination of optical flow and background subtract method. 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 the estimation more accurately:, the rate of true classification is 86.3% on a data set of 600 images.