An Efficient Algorithm Proposed For Smoke Detection in Video Using Hybrid Feature Selection Techniques

P. Matlani, M. Shrivastava


As an emerging development in the digital technology era, video processing is useful in a wide range of applications. In the current paper, an algorithm is proposed which is useful for smoke detection in video processing. The algorithm quickly detects fire by eliminating common interruptions like noise, overlapping due to the collision, etc. The proposed algorithm is composed of several techniques such as Haar feature, Bhattacharya distance method, SIFT descriptors, Gabor wavelets approach and SVM classifier to identify the smoke by video processing. Foreground object is identified using a moving object algorithm by predicting the movement of smoke in stable images. The implementation has been carried out in MATLAB.


smoke detection; Bhattacharya distance; video processing; bounding box technique; SIFT; Gabor wavelet approach; hybrid algorithm; hill climbing algorithm; moving object algorithm

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H. Tian, W. Li, L. Wang, P. Ogunbona, “A Novel Video-Based Smoke Detection Method Using Image Separation”, IEEE International Conference on Multimedia and Expo, Melbourne, Australia, July 9-13, 2012

M. Favorskaya, A. Pyataeva, A. Popov, “Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns”, Procedia Computer Science, Vol. 60, pp. 671-680, 2015

C. R. Steffens, R. N. Rodrigues S. S. D. C. Botelho, “An Unconstrained Dataset for Non-Stationary Video Based Fire Detection”, 12th Latin American Robotics Symposium and 3rd Brazilian Symposium on Robotics (LARS-SBR), Uberlandia, Brazil, October 29-31, 2015

Y. De-Fei, H. Ying, B. Feng-Long, “Video smoke detection based on semitransparent properties”, The 27th Chinese Control and Decision Conference, Qingdao, China, May 23-25, 2015

A. Benazza-Benyahia, N. Hamouda, F. Tlili, S. Ouerghi, “Early smoke detection in forest areas from DCT based compressed video”, 20th European Signal Processing Conference (EUSIPCO), Bucharest, Romania, August 27-31, 2012

R. D. Labati, A. Genovese, V. Piuri, F. Scotti, “Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 43, No. 4, pp. 1003-1012, 2013

C. Y. Lee, C. T. Lin, C. T. Hong, M. T. Su, “Smoke detection using spatial and temporal analyses”, International Journal of Innovative Computing, Information and Control, Vol. 8, No. 7, pp. 4749-4770, 2012

L. Miao, Y. Chen, A. Wang, “Video smoke detection algorithm using dark channel priori”, 33rd Chinese Control Conference, Nanjing, China, July 28-30, 2014

S. M. Ma, Y. Sun, A. P. Li, “A simulated annealing algorithm for multi-objective hybrid flow shop scheduling”, in: Design, Manufacturing and Mechatronics, pp. 1463-1473, World Scientific, 2015

T. Ohashi, Z. Aghbari, A. Makinouchi, “Hill-climbing algorithm for efficient color-based image segmentation”, IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, June 30-July 2, Rhodes, Greece, 2003

K. Dimitropoulos, P. Barmpoutis, N. Grammalidis, “Higher Order Linear Dynamical Systems for Smoke Detection in Video Surveillance Applications”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 27, No. 5, pp. 1143-1154, 2017

I. F. Ince, G. Y. Kim, G. H. Lee, J. S. Park, “Patch-wise periodical correlation analysis of histograms for real-time video smoke detection”, 2014 IEEE International Conference in Industrial Technology, Busan, South Korea, February 26-March 1, 2014

R. Bohush, N. Brouka, “Smoke and flame detection in video sequences based on static and dynamic features”, Signal Processing: Algorithms, Architectures, Arrangements, and Applications, Poznan, Poland, September 26-28, 2013

O. B. Alejandro, M. G. Leonardo, S. P. Gabriel, T. M. Karina, N. M. Mariko, “Improvement of a Video Smoke Detection Based on Accumulative Motion Orientation Model”, IEEE Electronics, Robotics and Automotive Mechanics Conference, Cuernavaca, Mexico, November 15-18, 2011

Y. Liu, G. Liu, “A smoke detection algorithm of energy difference between frames based on adaptive LOG operator on the infrared video processing”, Second International Conference on Mechanic Automation and Control Engineering, Hohhot, China, July 15-17, 2011

I. Kolesov, P. Karasev, A. Tannenbaum, E. Haber, “Fire and smoke detection in video with optimal mass transport based optical flow and neural networks”, 2010 IEEE International Conference on Image Processing, Hong Kong, China, September 26-29, 2010

D. Kim, Y. F. Wang, “Smoke detection in video”, 2009 Congress on Computer Science and Information Engineering, Los Angeles, USA, March31-April 2, 2009

C. Ho, T. Kuo, “Real-time video-based fire smoke detection system”, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, July 14-17, 2009

Z. Wei, X. Wang, W. An, J. Che, “Target-tracking based early fire smoke detection in video”, Fifth International Conference on Image and Graphics, Xi'an, China, September 20-23, 2009

T. H. Chen, Y. H. Yin, S. F. Huang, Y. T. Ye, “The smoke detection for early fire-alarming system base on video processing”, 2006 International Conference on Intelligent Information Hiding and Multimedia, Pasadena, USA, December 18-20, 2006

B. U. Toreyin, Y. Dedeoglu, U. Gudukbay, A. E. Cetin, “Computer vision based method for real-time fire and flame detection”, Pattern Recognition Letters, Vol. 27, No. 1, pp. 49-58, 2006

F. Yuan, “Video-based smoke detection with histogram sequence of LBP and LBPV pyramids”, Fire Safety Journal, Vol. 46, No. 3, pp. 132-139, 2011

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