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Silhouette-based multi-sensor smoke detection: coverage analysis of moving object silhouettes in thermal and visual registered images

Steven Verstockt UGent, Chris Poppe UGent, Sofie Van Hoecke UGent, Charles Hollemeersch UGent, Bart Merci UGent, Bart Sette, Peter Lambert UGent and Rik Van de Walle UGent (2012) MACHINE VISION AND APPLICATIONS. 23(6). p.1243-1262
abstract
Fire is one of the leading hazards affecting everyday life around the world. The sooner the fire is detected, the better the chances are for survival. Today's fire alarm systems, such as video-based smoke detectors, however, still pose many problems. In order to accomplish more accurate video-based smoke detection and to reduce false alarms, this paper proposes a multi-sensor smoke detector which takes advantage of the different kinds of information represented by visual and thermal imaging sensors. The detector analyzes the silhouette coverage of moving objects in visual and long-wave infrared registered ( aligned) images. The registration is performed using a contour mapping algorithm which detects the rotation, scale and translation between moving objects in the multi-spectral images. The geometric parameters found at this stage are then further used to coarsely map the silhouette images and coverage between them is calculated. Since smoke is invisible in long-wave infrared its silhouette will, contrarily to ordinary moving objects, only be detected in visual images. As such, the coverage of thermal and visual silhouettes will start to decrease in case of smoke. Due to the dynamic character of the smoke, the visual silhouette will also show a high degree of disorder. By focusing on both silhouette behaviors, the system is able to accurately detect the smoke. Experiments on smoke and non-smoke multi-sensor sequences indicate that the automated smoke detection algorithm is able to coarsely map the multi-sensor images. Furthermore, using the low-cost silhouette analysis, a fast warning, with a low number of false alarms, can be given.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Image registration, Coverage analysis, Multi-sensor, Multi-modal, Smoke detection, FEATURES, RECOGNITION, VIDEO, MUTUAL INFORMATION, FIRE DETECTION, SYSTEM, REGISTRATION
journal title
MACHINE VISION AND APPLICATIONS
Mach. Vis. Appl.
volume
23
issue
6
pages
1243 - 1262
Web of Science type
Article
Web of Science id
000309875400013
JCR category
ENGINEERING, ELECTRICAL & ELECTRONIC
JCR impact factor
1.103 (2012)
JCR rank
121/242 (2012)
JCR quartile
3 (2012)
ISSN
0932-8092
DOI
10.1007/s00138-011-0359-3
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1970409
handle
http://hdl.handle.net/1854/LU-1970409
date created
2011-12-20 09:54:43
date last changed
2013-10-22 11:45:57
@article{1970409,
  abstract     = {Fire is one of the leading hazards affecting everyday life around the world. The sooner the fire is detected, the better the chances are for survival. Today's fire alarm systems, such as video-based smoke detectors, however, still pose many problems. In order to accomplish more accurate video-based smoke detection and to reduce false alarms, this paper proposes a multi-sensor smoke detector which takes advantage of the different kinds of information represented by visual and thermal imaging sensors. The detector analyzes the silhouette coverage of moving objects in visual and long-wave infrared registered ( aligned) images. The registration is performed using a contour mapping algorithm which detects the rotation, scale and translation between moving objects in the multi-spectral images. The geometric parameters found at this stage are then further used to coarsely map the silhouette images and coverage between them is calculated. Since smoke is invisible in long-wave infrared its silhouette will, contrarily to ordinary moving objects, only be detected in visual images. As such, the coverage of thermal and visual silhouettes will start to decrease in case of smoke. Due to the dynamic character of the smoke, the visual silhouette will also show a high degree of disorder. By focusing on both silhouette behaviors, the system is able to accurately detect the smoke. Experiments on smoke and non-smoke multi-sensor sequences indicate that the automated smoke detection algorithm is able to coarsely map the multi-sensor images. Furthermore, using the low-cost silhouette analysis, a fast warning, with a low number of false alarms, can be given.},
  author       = {Verstockt, Steven and Poppe, Chris and Van Hoecke, Sofie and Hollemeersch, Charles and Merci, Bart and Sette, Bart and Lambert, Peter and Van de Walle, Rik},
  issn         = {0932-8092},
  journal      = {MACHINE VISION AND APPLICATIONS},
  keyword      = {Image registration,Coverage analysis,Multi-sensor,Multi-modal,Smoke detection,FEATURES,RECOGNITION,VIDEO,MUTUAL INFORMATION,FIRE DETECTION,SYSTEM,REGISTRATION},
  language     = {eng},
  number       = {6},
  pages        = {1243--1262},
  title        = {Silhouette-based multi-sensor smoke detection: coverage analysis of moving object silhouettes in thermal and visual registered images},
  url          = {http://dx.doi.org/10.1007/s00138-011-0359-3},
  volume       = {23},
  year         = {2012},
}

Chicago
Verstockt, Steven, Chris Poppe, Sofie Van Hoecke, Charles Hollemeersch, Bart Merci, Bart Sette, Peter Lambert, and Rik Van de Walle. 2012. “Silhouette-based Multi-sensor Smoke Detection: Coverage Analysis of Moving Object Silhouettes in Thermal and Visual Registered Images.” Machine Vision and Applications 23 (6): 1243–1262.
APA
Verstockt, S., Poppe, C., Van Hoecke, S., Hollemeersch, C., Merci, B., Sette, B., Lambert, P., et al. (2012). Silhouette-based multi-sensor smoke detection: coverage analysis of moving object silhouettes in thermal and visual registered images. MACHINE VISION AND APPLICATIONS, 23(6), 1243–1262.
Vancouver
1.
Verstockt S, Poppe C, Van Hoecke S, Hollemeersch C, Merci B, Sette B, et al. Silhouette-based multi-sensor smoke detection: coverage analysis of moving object silhouettes in thermal and visual registered images. MACHINE VISION AND APPLICATIONS. 2012;23(6):1243–62.
MLA
Verstockt, Steven, Chris Poppe, Sofie Van Hoecke, et al. “Silhouette-based Multi-sensor Smoke Detection: Coverage Analysis of Moving Object Silhouettes in Thermal and Visual Registered Images.” MACHINE VISION AND APPLICATIONS 23.6 (2012): 1243–1262. Print.