Integrated probabilistic generative model for detecting smoke on visual images

Vidal-Calleja, TA; Agammenoni, G

Integrated probabilistic generative model for detecting smoke on visual images

Vidal-Calleja, TA

Agammenoni, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2012, pp. 2183 - 2188
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Vidal-Calleja, TA https://orcid.org/0000-0002-5763-9644	en_US
dc.contributor.author	Agammenoni, G	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2012, pp. 2183 - 2188	en_US
dc.identifier.isbn	9781467314039	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23602
dc.identifier.uri	http://hdl.handle.net/10453/30408
dc.description.abstract	Early fire detection is crucial to minimise damage and save lives. Video surveillance smoke detectors do not suffer from transport delays and can cover large areas. The smoke detection on images is, however, a difficult problem due the variability of smoke density, lighting conditions, background clutter, and unstable patterns. In order to solve this problem, we propose a novel unsupervised object classifier. Single visual features are classified using a model that simultaneously creates a codebook and categorises the smoke using a bag-of-words paradigm based on LDA model. Our algorithm can also tell the amount of smoke present on the image. Multiple image sequences from different cameras are used to show the viability of the proposed approach. Our experiments show that the model generalises well for different cameras, perspectives and scales. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2012.6225096	en_US
dc.title	Integrated probabilistic generative model for detecting smoke on visual images	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	St Paul, MN	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Early fire detection is crucial to minimise damage and save lives. Video surveillance smoke detectors do not suffer from transport delays and can cover large areas. The smoke detection on images is, however, a difficult problem due the variability of smoke density, lighting conditions, background clutter, and unstable patterns. In order to solve this problem, we propose a novel unsupervised object classifier. Single visual features are classified using a model that simultaneously creates a codebook and categorises the smoke using a bag-of-words paradigm based on LDA model. Our algorithm can also tell the amount of smoke present on the image. Multiple image sequences from different cameras are used to show the viability of the proposed approach. Our experiments show that the model generalises well for different cameras, perspectives and scales. © 2012 IEEE.

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http://hdl.handle.net/10453/23602