Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

Fire hazards occurring recently in the world lead to the need of designing accurate fire detection systems in order to save human lives. The newest innovations continue to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications like the adaboost classifier which is well known for its strength in rigid objects detection from images. This paper presents a Fire Detection System (FDS) with an algorithm that works side by side with the adaboost classifier to determine the presence of fire in an image taken by a normal web camera (webcam), in order to decrease the false alarms in an indoor scene. The images are first preprocessed and their selected discrete cosine coefficients are kept for analysis to get better coefficients that will be fed to a neural network for classification and results are compared to a statistical approach used in combination with binary background mask (BBM) and a wavelet-based model of fire’s frequency signature(WMF) to test its accuracy

[1]  Moulay A. Akhloufi,et al.  Dynamic Fire 3D Modeling Using a Real-Time Stereovision System , 2008, EIAT/IETA.

[2]  Riad I. Hammoud,et al.  Disparity Based Image Segmentation For Occupant Classification , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[3]  ByoungChul Ko,et al.  Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks , 2010 .

[4]  ByoungChul Ko,et al.  Fire-Flame Detection Based on Fuzzy Finite Automation , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Bo Fu,et al.  A Video Smoke Detection Algorithm Based on Wavelet Energy and Optical Flow Eigen-values , 2013 .

[6]  Jong-Myon Kim,et al.  Fire Detection with Video Using Fuzzy c-Means and Back-Propagation Neural Network , 2011, ISNN.

[7]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[8]  M. Akhloufi,et al.  Dynamic fire modeling in Three-dimensional space , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

[9]  Hsuan-Ming Feng,et al.  A self-tuning fuzzy control system design , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[10]  Bo Fu,et al.  A Block-based Video Smoke Detection Algorithm , 2013, J. Softw..

[11]  Marimuthu Palaniswami,et al.  Smoke detection in video using wavelets and support vector machines , 2009 .

[12]  Nong Sang,et al.  Robust Automatic Facial Expression Detection Method , 2013, J. Softw..

[13]  W. Marsden.  I and J , 2012 .

[14]  Sung-Hwan Jung,et al.  Image Processing-Based Fire Detection System Using Statistic Color Model , 2008, 2008 International Conference on Advanced Language Processing and Web Information Technology.

[15]  Turgay Celik,et al.  Fast and Efficient Method for Fire Detection Using Image Processing , 2010 .

[16]  Tong Wang,et al.  Fire and Explosion Hazard Prediction Base on Virtual Reality in Tank Farm , 2012, J. Softw..

[17]  José Barata,et al.  A vision-based system for early fire detection , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[18]  Wang Dong,et al.  An Early Fire Image Detection and Identification Algorithm Based on DFBIR Model , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.