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Computer Vision Based Measurement of Wildfire Smoke DynamicsBUGARIC, M. , JAKOVCEVIC, T. , STIPANICEV, D.
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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)
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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 7]
 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.
 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.
 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.
 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]
 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.
 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.
 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]
 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]
 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]
 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]
 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]
 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.
 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]
 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]
 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 1262]
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