Fire flame detection in video sequences using multi-stage pattern recognition techniques

Engineering Applications of Artificial Intelligence

Volumn 25, Issue 7, 2012, Pages 1365-1372

  Xuan Truong, Tung ^a   Kim, Jong Myon ^a  

^a University of Ulsan   (South Korea)

Author keywords

Fire detection; Fuzzy c means; Mixture of Gaussian model; Support vector machines

Indexed keywords

ADAPTIVE GAUSSIAN MIXTURE; FALSE ALARM RATE; FIRE DETECTION; FIRE FLAME; FUZZY C MEAN; FUZZY C-MEANS ALGORITHMS; HIGH RELIABILITY; MIXTURE OF GAUSSIANS; MOVING REGIONS; MULTI-STAGE; PATTERN RECOGNITION TECHNIQUES; SUPPORT VECTOR MACHINE ALGORITHM; VIDEO IMAGE; VIDEO SEQUENCES;

ALGORITHMS; FIRE DETECTORS; FUZZY SYSTEMS; IMAGE SEGMENTATION; OBJECT RECOGNITION; SUPPORT VECTOR MACHINES;

FIRES;

EID: 84866741474     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2012.05.007     Document Type: Article

References (28)

1. P.V.K. Borges, and E. Izquierdo A probabilistic approach for vision-based fire detection in videos IEEE. Trans. Circuits Syst. Video Technol. 20 2010 721 731
2. J.C. Bezdek Pattern recognition with fuzzy objective function algorithms 1981 Pleum Press New York
3. J.C. Bezdek, J. Keller, R. Krisnapuram, and N. Pal Fuzzy models and algorithms for pattern recognition and image processing 2005 Springer
4. V. Bhaskaran, and K. Konstantinides Image and video compression standards: algorithms and architectures 1997 Kluwer Academic Publishers 410-412
5. Bobick, A.; Davis, J.; 1996. Real-time Recognition of Activity using Temporal Templates. In: IEEE Workshop on Applications of Computer Vision, pp. 39-42.
6. C.J.C. Burges A tutorial on support vector machines for pattern recognition J. Knowl. Discovery Data Mining 2 1998 121 167
7. Chen, T.H.; Wu, P.H.; Chiou, Y.C.; 2004. An Early Fire-Detection Method based on Image Processing. In: IEEE. Int. Conf. on Image Processing, vol. 3, pp. 1707-1710.
8. T. Celik, and H. Demirel Fire detection in video sequences using a generic color model Fire Saf. J. 44 2009 147 158
9. T. Celik, H. Demirel, H. Ozkaramanli, and M. Uyguroglu Fire detection using statistical color model in video sequences J. Visual Commun. Rep. 18 2007 176 185
10. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press
11. G.W. Corder, and D.I. Foreman Nonparametric statistics for non-statisticians: a step-by-step approach 2009 Wiley
12. Davis, J.; Bobick, A.; 1997. The Representation and Recognition of Action using Temporal Templates. Technical Report 402, MIT Media Lab, Cambridge, MA, 1.
13. Davis, J.; Bradski, G.; 2009. Real-time Motion Template Gradients using Intel CVLib. ICCV Workshop on Framerate Vision.
14. D. Han, and B. Lee Development of early tunnel fire detection algorithm using the image processing Adv. Visual Comput. 2006 39 48
15. M.K. Hu Visual pattern recognition by moment invariants IRE Trans. Inf. Theory 8 1962 179 187
16. Jones, W.W.; 2004. An Algorithm for Fast and Reliable Fire Detection. In: 8th Fire Suppression and Detection Research Application Symposium, pp. 1-3.
17. B.C. Ko, K.H. Cheong, and J.Y. Nam Fire detection based on vision sensor a support vector machines Fire Saf. J. 44 2009 322 329
18. KaewTraKulPong, P.; Bowden, R.; 2002. An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection. In: Proc. 2nd European Workshop on Advanced Video-Based Surveillance System, AVBS01, pp. 135-144.
19. Piccardi, M.; 2004. Background Subtraction Techniques: A Review. In: IEEE. Int. Conf. on Systems. Man and Cybernetics, pp. 3099-3104.
20. Platt, J.C.; 1998. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Microsoft Research Technical Report MSR-TR-98-14.
21. Stauffer, C.; Grimson, W.E.L.; 1999. Adaptive Background Mixture Models for Real-Time Tracking. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252.
22. C. Stauffer, and W.E.L. Grimson Learning patterns of activity using real-time tracking IEEE Trans. Pattern Anal. Machines Intel. 22 2000 747 757
23. Toreyin, B.U.; Centin, A.E.; 2007. Online Detection of Fire in Video. In: IEEE. Conf. on Computer Vision and Pattern Recognition, pp. 1-5.
24. B.U. Toreyin, Y. Dedeoglu, U. Gudukbay, and A.E. Centin Computer vision-based method for real-time fire and flame detection Pattern Recog. Lett. 27 2006 49 58
25. K.S. Tan, and N.A.M. Isa Color image segmentation using histogram thresholding-fuzzy c-means hybrid approach Pattern Recognition 44 2010 1 15
26. V. Vapnik Estimation of dependences based on empirical data 1982 Springer-Verlag
27. V. Vapnik Statistical learning theory 1995 Springer New York
28. F. Wilcoxon Individual comparisons by ranking methods Biometrics Bull. 1945 80 83