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

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Authors: Sabeur, Z., Doulamis, N., Middleton, L., Arbab-Zavar, B., Correndo, G. and Amditis, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 9474

Pages: 162-173

eISSN: 1611-3349

ISBN: 9783319278568

ISSN: 0302-9743

DOI: 10.1007/978-3-319-27857-5_15

© Springer International Publishing Switzerland 2015. 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.

This data was imported from Web of Science (Lite):

Authors: Sabeur, Z., Doulamis, N., Middleton, L., Arbab-Zavar, B., Correndo, G. and Amditis, A.

Journal: ADVANCES IN VISUAL COMPUTING, PT I (ISVC 2015)

Volume: 9474

Pages: 162-173

eISSN: 1611-3349

ISBN: 978-3-319-27856-8

ISSN: 0302-9743

DOI: 10.1007/978-3-319-27857-5_15

The data on this page was last updated at 05:13 on February 22, 2020.