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Multi-modal computer vision for the detection of multi-scale crowd physical motions and behavior in confined spaces

Multi-modal computer vision for the detection of multi-scale crowd physical motions and behavior in confined spaces
Multi-modal computer vision for the detection of multi-scale crowd physical motions and behavior in confined spaces
Crowd physical motion and behaviour detection during evacuation from confined spaces using computer vision is the main focus of research in the eVACUATE project. Its early foundations and development perspectives are discussed in this paper. Specifically, the main target in our development is to achieve good rates of correct detection and classification of crowd motion and behaviour in confined spaces respectively. However, the performance of the computer vision algorithms, which are put in place for the detection of crowd motion and behaviour, greatly depends on the quality, including causality, of the multi-modal observation data with ground truth. Furthermore, it is of paramount importance to take into account contextual information about the confined spaces concerned in order to confirm the type of detected behaviours. The pilot venues for crowd evacuation experimentations include: (1) Athens International Airport, Greece; (2) An underground train station in Bilbao, Spain; (3) A stadium in San Sebastian, Spain; and (4) A large cruise ship in St. Nazaire, France.
162-173
Springer
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Middleton, Lee J.
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Doulamis, Nikolaos
ebb8fc29-abdd-4513-9040-17ff82331e07
Amditis, Aggelos
7026f3e4-ad0f-4122-9788-3636ff975823
Bebis, G.
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Middleton, Lee J.
f165a2fa-1a66-4d84-9c58-0cdaa8e73272
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Doulamis, Nikolaos
ebb8fc29-abdd-4513-9040-17ff82331e07
Amditis, Aggelos
7026f3e4-ad0f-4122-9788-3636ff975823
Bebis, G.

Sabeur, Zoheir, Middleton, Lee J., Arbab-Zavar, Banafshe, Correndo, Gianluca, Doulamis, Nikolaos and Amditis, Aggelos (2015) Multi-modal computer vision for the detection of multi-scale crowd physical motions and behavior in confined spaces. Bebis, G. (ed.) In Advances in Visual Computing. vol. 9474, Springer. pp. 162-173 . (doi:10.1007/978-3-319-27857-5_15).

Record type: Conference or Workshop Item (Paper)

Abstract

Crowd physical motion and behaviour detection during evacuation from confined spaces using computer vision is the main focus of research in the eVACUATE project. Its early foundations and development perspectives are discussed in this paper. Specifically, the main target in our development is to achieve good rates of correct detection and classification of crowd motion and behaviour in confined spaces respectively. However, the performance of the computer vision algorithms, which are put in place for the detection of crowd motion and behaviour, greatly depends on the quality, including causality, of the multi-modal observation data with ground truth. Furthermore, it is of paramount importance to take into account contextual information about the confined spaces concerned in order to confirm the type of detected behaviours. The pilot venues for crowd evacuation experimentations include: (1) Athens International Airport, Greece; (2) An underground train station in Bilbao, Spain; (3) A stadium in San Sebastian, Spain; and (4) A large cruise ship in St. Nazaire, France.

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Published date: 2015

Identifiers

Local EPrints ID: 420237
URI: http://eprints.soton.ac.uk/id/eprint/420237
PURE UUID: fced68e5-3691-45bc-9b08-a24722441b8b
ORCID for Zoheir Sabeur: ORCID iD orcid.org/0000-0003-4325-4871
ORCID for Gianluca Correndo: ORCID iD orcid.org/0000-0003-3335-5759

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Date deposited: 03 May 2018 16:30
Last modified: 07 Oct 2019 17:00

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