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Physical-barrier detection based collective motion analysis |
Gaoqi HE1,2, Qi CHEN1, Dongxu JIANG1, Yubo YUAN1, Xingjian LU1,3( ) |
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 |
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Abstract 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.Comparedwith the current collectivemotion analysis methods, our approach better adapts to the scenes with physical barriers.
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Keywords
crowd behavior analysis
collective motion
physical-barrier detection
two-stage clustering
local region collectiveness
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Corresponding Author(s):
Xingjian LU
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Just Accepted Date: 09 October 2017
Online First Date: 06 March 2018
Issue Date: 08 April 2019
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