A novel social distancing analysis in urban public space: A new online spatio-temporal trajectory approach.
Crowd gathering
Discrete Fréchet distance
Hierarchical data association
Multi-pedestrian tracking
Spatio-temporal trajectory
Visual social distancing
Journal
Sustainable cities and society
ISSN: 2210-6715
Titre abrégé: Sustain Cities Soc
Pays: Netherlands
ID NLM: 101735304
Informations de publication
Date de publication:
May 2021
May 2021
Historique:
received:
22
08
2020
revised:
16
01
2021
accepted:
30
01
2021
entrez:
15
2
2021
pubmed:
16
2
2021
medline:
16
2
2021
Statut:
ppublish
Résumé
Social distancing in public spaces plays a crucial role in controlling or slowing down the spread of coronavirus during the COVID-19 pandemic. Visual Social Distancing (VSD) offers an opportunity for real-time measuring and analysing the physical distance between pedestrians using surveillance videos in public spaces. It potentially provides new evidence for implementing effective prevention measures of the pandemic. The existing VSD methods developed in the literature are primarily based on frame-by-frame pedestrian detection, addressing the VSD problem from a static and local perspective. In this paper, we propose a new online multi-pedestrian tracking approach for spatio-temporal trajectory and its application to multi-scale social distancing measuring and analysis. Firstly, an online multi-pedestrian tracking method is proposed to obtain the trajectories of pedestrians in public spaces, based on hierarchical data association. Then, a new VSD method based on spatio-temporal trajectories is proposed. The proposed method not only considers the Euclidean distance between tracking objects frame-by-frame but also takes into account the discrete Fréchet distance between trajectories, hence forms a comprehensive solution from both static and dynamic, local and holistic perspectives. We evaluated the performance of the proposed tracking method using the public dataset MOT16 benchmark. We also collected our own pedestrian dataset "SCU-VSD" and designed a multi-scale VSD analysis scheme for benchmarking the performance of the social distancing monitoring in the crowd. Experiments have demonstrated that the proposed method achieved outstanding performance on the analysis of social distancing.
Identifiants
pubmed: 33585169
doi: 10.1016/j.scs.2021.102765
pii: S2210-6707(21)00055-X
pmc: PMC7865092
doi:
Types de publication
Journal Article
Langues
eng
Pagination
102765Informations de copyright
© 2021 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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