文献类型：期刊文献

英文题名：Physical-barrier detection based collective motion analysis

作者：He, Gaoqi[1,2];Chen, Qi[1];Jiang, Dongxu[1];Yuan, Yubo[1];Lu, Xingjian[1,3]

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;[3]Shanghai Jiao Tong Univ, Smart City Collaborat Innovat Ctr, Shanghai 200240, Peoples R China

年份：2019

卷号：13

期号：2

起止页码：426

外文期刊名：FRONTIERS OF COMPUTER SCIENCE

收录：;EI(收录号：20180704801773);WOS:【SCI-EXPANDED(收录号:WOS:000464846400016)】；

基金：This work was funded by the National Key Research and Development Program of China (2016YFA0502300), the National Natural Science Foundation of China (Grant No. 61602175), Shanghai Municipal Commission of Economy and Informatization (150809), the Open Research Funding Program of KLGIS (KLGIS2015A05) and BUAA (BUAAVR-15KF-03), the Fundamental Research Funds for the Central Universities (222201514331), and Green Manufacturing System Integration Project of Ministry of Industry and Technology of China (9908000006).

语种：英文

外文关键词：crowd behavior analysis; collective motion; physical-barrier detection; two-stage clustering; local region collectiveness

摘要：Collective motion is one of the most fascinating phenomena and mainly caused by the interactions between individuals. Physical-barriers, as the particular facilities which divide the crowd into different lanes, greatly affect the measurement of such interactions. In this paper we propose the physical-barrier detection based collective motion analysis (PDCMA) approach. The main idea is that the interaction between spatially adjacent pedestrians actually does not exist if they are separated by the physical-barrier. Firstly, the physical-barriers are extracted by two-stage clustering. The scene is automatically divided into several motion regions. Secondly, local region collectiveness is calculated to represent the interactions between pedestrians in each region. Finally, extensive evaluations use the three typical methods, i.e., the PDCMA, the Collectiveness, and the average normalized Velocity, to show the efficiency and efficacy of our approach in the scenes with and without physical barriers. Moreover, several escalator scenes are selected as the typical physical-barrier test scenes to demonstrate the performance of our approach. Compared with the current collective motion analysis methods, our approach better adapts to the scenes with physical barriers.