文献类型：期刊文献

英文题名：A double-region learning algorithm for counting the number of pedestrians in subway surveillance videos

作者：He, Gaoqi[1,2];Chen, Qi[1];Jiang, Dongxu[1];Lu, Xingjian[1];Yuan, Yubo[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China

年份：2017

卷号：64

起止页码：302

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20173003979845);WOS:【SCI-EXPANDED(收录号:WOS:000412378800026)】；

基金：This work was funded by the National Key Research and Development Program of China (Grant No: 2016YFA0502300) and Natural Science Foundation of China (Grant No: 61602175), Shanghai Municipal Commission of Economy and Informatization (Grant No: 150809), the Open Research Funding Program of KLGIS (Grant No: KLGIS2015A05) and BUAA (Grant No: BUAA-VR-15KF-03), the Fundamental Research Funds for the Central Universities (Grant No: 222201514331).

语种：英文

外文关键词：Video processing; Double-region learning; Extreme learning machine; Perspective distortion; Subway surveillance videos

摘要：Counting pedestrians in surveillance videos has become an urgent safety concern in critical areas. However, surveillance videos of subway spaces suffer from severe crowd occlusion and perspective distortion. In this paper, a novel double-region learning algorithm is presented to overcome these challenges. The main idea of this algorithm is to identify the best two-region boundary and then design a reasonable pedestrian-counting method in each separated region. First, a separate line is obtained via possibility learning, and each frame is divided into a nearby region and a distant region to eliminate the influence of perspective distortion. Second, in the nearby region, we apply the improved aggregate channel feature detection to count the number of pedestrians N-1. In the distant region, we employ the Extreme Learning Machine and Gaussian Process regression methods to estimate the number of pedestrians N-2. Finally, the total number of pedestrians in each frame can be obtained with high accuracy according to N-1 and N-2. We establish a subway pedestrian video dataset about several typical subway stations in Shanghai to validate the algorithm performance. Various experimental results demonstrate that the accuracy of the proposed approach surpasses that of compared methods, which means that our algorithm can meet the management requirements of subway stations. (C) 2017 Elsevier Ltd. All rights reserved.