文献类型：期刊文献

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

作者：Gaoqi HE[1,2];Qi CHEN[1];Dongxu JIANG[1];Yubo YUAN[1];Xingjian LU[1,3]

机构：[1]Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;[2]State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;[3]Smart City Collaborative Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China

年份：2019

卷号：13

期号：2

起止页码：426

中文期刊名：Frontiers of Computer Science

外文期刊名：中国计算机科学前沿（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2019_2020】；

基金：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);Green Manufacturing System Integration Project of Ministry of Industry and Technology of China (9908000006).

语种：英文

中文关键词：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.