Remote monitoring of an omnidirectional smoke detection system using texture features image processing techniques

Purpose – The purpose of this paper is to develop an early fire-alarm raising system based on video processing, and combine it with the omnidirectional projecting system. It not only gives alarm immediately in early fire so that people can be able to strive for more time to escape from the spot, but also solves problem of discontinued screen which was presented fire scene. Design/methodology/approach – The smoke detection system is made by image processing. The flowchart of smoke detection is improved, which the method of background updating can filter out the moving objects that only stay for a short time in the image; and avoids these objects being determined to be the background. Moreover, the authors extract the flickering area to separate the non-smoke and smoke from the candidate of smoke regions. Finally, the image processing is applied in omnidirectional projecting system, then presented the 360-degree fire scene. Findings – The results show that the smoke detection system can accurately detect th...

[1]  Feiniu Yuan,et al.  A fast accumulative motion orientation model based on integral image for video smoke detection , 2008, Pattern Recognit. Lett..

[2]  Frederick W. Williams,et al.  Long wavelength video detection of fire in ship compartments , 2006 .

[3]  Chao-Ho Chen,et al.  The smoke detection for early fire-alarming system base on video processing , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[4]  Zheng Wei,et al.  Target-Tracking Based Early Fire Smoke Detection in Video , 2009, 2009 Fifth International Conference on Image and Graphics.

[5]  A. Enis Çetin,et al.  Wavelet based real-time smoke detection in video , 2005, 2005 13th European Signal Processing Conference.

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

[7]  Tamás Szirányi,et al.  Application of panoramic annular lens for motion analysis tasks: surveillance and smoke detection , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[9]  Fang Jun,et al.  Texture Analysis of Smoke for Real-Time Fire Detection , 2009, 2009 Second International Workshop on Computer Science and Engineering.

[10]  Jing Yang,et al.  Visual-Based Smoke Detection Using Support Vector Machine , 2008, 2008 Fourth International Conference on Natural Computation.

[11]  Kuei-Shu Hsu,et al.  Development of a New Single Beam Omnidirectional Projector and its Application in Tele-robotic System , 2011 .

[12]  Chen-Yu Lee,et al.  Spatio-temporal analysis in smoke detection , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[13]  Simone Calderara,et al.  Smoke Detection in Video Surveillance: A MoG Model in the Wavelet Domain , 2008, ICVS.

[14]  Jr-Syu Yang,et al.  Reducing False Alarm of Video-Based Smoke Detection by Support Vector Machine , 2008, ISI Workshops.

[15]  Chao-Ching Ho.  Machine vision-based real-time early flame and smoke detection , 2009 .

[16]  H. Maruta,et al.  Smoke detection in open areas using its texture features and time series properties , 2009, 2009 IEEE International Symposium on Industrial Electronics.