|1/2015 - 8|
Computer Vision Based Measurement of Wildfire Smoke DynamicsBUGARIC, M. , JAKOVCEVIC, T. , STIPANICEV, D.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (882 KB) | Citation | Downloads: 237 | Views: 1,063|
image motion analysis, computer vision, computer aided analysis, virtual reality, pattern analysis
smoke(16), detection(14), fire(8), wildfire(6), visual(5), computational(5), video(4), spatial(4), image(4), forest(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 55 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01008
Web of Science Accession Number: 000352158600008
SCOPUS ID: 84924804457
This article presents a novel method for measurement of wildfire smoke dynamics based on computer vision and augmented reality techniques. The aspect of smoke dynamics is an important feature in video smoke detection that could distinguish smoke from visually similar phenomena. However, most of the existing smoke detection systems are not capable of measuring the real-world size of the detected smoke regions. Using computer vision and GIS-based augmented reality, we measure the real dimensions of smoke plumes, and observe the change in size over time. The measurements are performed on offline video data with known camera parameters and location. The observed data is analyzed in order to create a classifier that could be used to eliminate certain categories of false alarms induced by phenomena with different dynamics than smoke. We carried out an offline evaluation where we measured the improvement in the detection process achieved using the proposed smoke dynamics characteristics. The results show a significant increase in algorithm performance, especially in terms of reducing false alarms rate. From this it follows that the proposed method for measurement of smoke dynamics could be used to improve existing smoke detection algorithms, or taken into account when designing new ones.
|References|||||Cited By «-- Click to see who has cited this paper|
| E. Kuhrt, J. Knollenberg, V. Mertens, "An automatic early warning system for forest fires", Annals of Burns and Fire Disasters, vol. 14, no. 3, pp. 151-155, 2001.
 B. U. Toreyin, Y. Dedeoglu, A. E. Cetin, "Contour based smoke detection in video using wavelets", in European Signal Processing Conference, EUSIPCO, Florence, 2006, pp. 1-5.
 P. Piccinini, S. Calderara, R. Cucchiara, "Reliable smoke detection in the domains of image energy and color", in IEEE International Conference on Image Processing, 2008, pp. 1376-1379.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 22]
 A. Genovese, R. D. Labati, V. Piuri, F. Scotti, "Wildfire smoke detection using computational intelligence techniques", in IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), 2011, pp. 1-6.
[CrossRef] [SCOPUS Record]
 A. Ochoa-Brito, L. Millan-Garcia, G. Sanchez-Perez, K. Toscano-Medina, M. Nakano-Miyatake, "Improvement of a Video Smoke Detection Based on Accumulative Motion Orientation Model", in Electronics, Robotics and Automotive Mechanics Conference (CERMA) IEEE, 2011, pp. 126-130.
[CrossRef] [SCOPUS Times Cited 1]
 M. Anton, K. Olga, "Real-Time Smoke Detection in Video Sequences: Combined Approach, Lecture Notes in Computer Science", Springer Berlin Heidelberg, 2013, pp. 445-450.
[CrossRef] [SCOPUS Record]
 W. Meng-Yu, H. Ning, L. Qin-Juan, "A Smoke Detection Algorithm Based on Discrete Wavelet Transform and Correlation Analysis", in International Conference on Multimedia Information Networking and Security (MINES), 2012, pp. 281-284.
[CrossRef] [Web of Science Record] [SCOPUS Record]
 Y. Chunyu, Z. Yongming, F. Jun, W. Jinjun, "Texture Analysis of Smoke for Real-Time Fire Detection", in International Workshop on Computer Science and Engineering (WCSE), 2009, pp. 511-515.
[CrossRef] [SCOPUS Times Cited 11]
 Z. Xu, J. Xu, "Automatic Fire Smoke Detection Based on Image Visual Features", in International Conference on Computational Intelligence and Security Workshops, CISW, 2007, pp. 316-319.
 J. Y. Kwak, B. C. Ko, J. Y. Nam, "Forest Smoke Detection Using CCD Camera and Spatial-temporal Variation of Smoke Visual Patterns", in International Conference on Computer Graphics, Imaging and Visualization (CGIV), 2011, pp. 141-144.
[CrossRef] [SCOPUS Times Cited 3]
 J. Yang, F. Chen, W. Zhang, "Visual-Based Smoke Detection Using Support Vector Machine", in International Conference on Natural Computation, 2008. ICNC '08., 2008, pp. 301-305.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 17]
 K. B. McGrattan, H. R. Baum, R. G. Rehm, "Numerical simulation of smoke plumes from large oil fires", Atmospheric Environment, vol. 30, no. 24, pp. 4125-4136, 1996.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 50]
 W. Chow, R. Yin, "A new model on simulating smoke transport with computational fluid dynamics", Building and Environment, vol. 39, no. 6, pp. 611-620, 2004.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 19]
 H. M. Chang, A. F. Ghoniem, "A Computational Model for the Rise and Dispersion of Wind-blown, Buoyancy-driven Plumes. Part II.: Linearly Stratified Atmosphere", U.S. Department of Commerce, National Institute of Standards and Technology, MA, Cambridge, 1993.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 17]
 M. Castrillon, P. A. Jorge, I. J. Lopez, A. Macias, D. Martin, R. J. Nebot, et al., "Wildfire Prevention and Management in a 3D Virtual Environment", in GeoInformatics for Environmental Surveillance, statGIS09, Miloc, 2009.
 M. Castrillon, P. Jorge, I. Lopez, A. Macias, D. Martin, R. Nebot, et al., "Forecasting and visualization of wildfires in a 3D geographical information system", Computers & Geosciences, vol. 37, no. 3, pp. 390-396, 2011.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 11]
 S. Yun, C. Chen, J. Li, L. Tang, "Wildfire spread simulation and visualization in virtual environments", in IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM, Fuzhou, 2011, pp. 315-319.
[CrossRef] [SCOPUS Times Cited 5]
 M. Castrillon, P. A. Jorge, A. Macias, A. J. Sanchez, J. Sanchez, J. P. Suarez, et al., "Wildfire forecasting using an open source 3D multilayer geographical framework", in International Conference and Exhibition on Computer Graphics and Interactive Techniques, SIGGRAPH2009, New Orleans, 2009.
 S. Matthews, A. Sullivan, J. Gould, R. Hurley, P. Ellis, J. Larmour, "Evaluation of three fire detection systems", Bushfire Cooperative Research Centre, 2010.
 F. X. Catry, F. C. Rego, T. Santos, J. Almeida, P. Relvas, "Forest fires prevention in Portugal-Using GIS to help improving early fire detection effectiveness", in International Wildland Fire Conference, Seville, 2007.
 R. T. Collins, A. J. Lipton, T. Kanade, "A system for video surveillance and monitoring", in American Nuclear Society (ANS) Eight International Topical Meeting on Robotics and Remote Systems, Pittsburgh, Pennsylvania, 1999.
 T. Jakovcevic, D. Stipanicev, D. Krstinic, "Visual spatial-context based wildfire smoke sensor", Machine vision and applications, vol. 24, no. 4, pp. 707-719, 2012.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 5]
 M. Bugaric, T. Jakovcevic, D. Stipanicev, "Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index", Computer Vision and Image Understanding, vol. 118, pp. 184-196., 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 3]
 T. Jakovcevic, L. Bodrozic, D. Stipanicev, D. Krstinic, "Wildfire smoke-detection algorithms evaluation", in International Conference on Forest Fire Research, 2010, pp. 1-12.
 B. W., Matthews, "Comparison of the predicted and observed secondary structure of T4 phage lysozyme", Biochimica et Biophysica Acta, vol. 405, Issue 2, pp. 442-451, 1975.
[CrossRef] [Web of Science Times Cited 1232] [SCOPUS Times Cited 1442]
Web of Science® Citations for all references: 1,333 TCR
SCOPUS® Citations for all references: 1,606 TCR
Web of Science® Average Citations per reference: 51 ACR
SCOPUS® Average Citations per reference: 62 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2017-01-18 13:46 in 104 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.