Effective Smoke Detection Using Spatial-Temporal Energy and Weber Local Descriptors in Three Orthogonal Planes (WLD-TOP)

  • John Adedapo Ojo Department of Electronic & Electrical Engineering, Ladoke Akintola University of Technology, P.M.B 4000, Ogbomoso, Nigeria.
  • Jamiu Alabi Oladosu Department of Electronic & Electrical Engineering, Ladoke Akintola University of Technology, P.M.B 4000, Ogbomoso, Nigeria.
Keywords: Video-based smoke detection, Weber Local Descriptor, Three Orthogonal Planes, Dynamic texture descriptors, Support Vector Machine


Video-based fire detection (VFD) technologies have received significant attention from both academic and industrial communities recently. However, existing VFD approaches are still susceptible to false alarms due to changes in illumination, camera noise, variability of shape, motion, colour, irregular patterns of smoke and flames, modelling and training inaccuracies. Hence, this work aimed at developing a VSD system that will have a high detection rate, low false-alarm rate and short response time. Moving blocks in video frames were segmented and analysed in HSI colour space, and wavelet energy analysis of the smoke candidate blocks was performed. In addition, Dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP). These features were combined and used as inputs to Support Vector Classifier with radial based kernel function, while post-processing stage employs temporal image filtering to reduce false alarm. The algorithm was implemented in MATLAB (R2013a). Accuracy of 99.30%, detection rate of 99.28% and false alarm rate of 0.65% were obtained when tested with some online videos. The output of this work would find applications in early fire detection systems and other applications such as robot vision and automated inspection.


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[1] E.A. Çetin, K. Dimitropoulos, B. Gouverneur, H.Y. Habiboglu, B.U. Töreyin, and S. Verstockt, et al., “Video fire detection – review,” Digital Signal Processing, vol. 23, no. 6, pp. 1827–1843, 2013.
[2] J. Chen and Y. You, “Early Fire Detection Using HEP and Space-time Analysis,” Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) arXiv:1310.1855, pp. 228 –312, 2013.
[3] J. Chen, Y. You, and Q. Peng, “Dynamic analysis for video-based smoke detection,” International Journal of Computer Science Issues, vol. 10, no. 2, pp. 298–304, 2013.
[4] L. Wen-hui, F. Bo, C. Xiao-lin, W. Ying, and L. Pei-xun, “A Block-based Video Smoke Detection Algorithm,” Journal of Jilin University (Science Edition), vol. 50, no. 5, pp. 979-986, 2012.
[5] J. Gubbi, S. Marusic, and M. Palaniswami, “Smoke detection in video using wavelets and support vector machines,” Fire Safety Journal, vol. 44, no.8, pp. 1110 –1115, (2009).
[6] R.A. Gonzalez-Gonzalez, V. Alarcon-Aquino, O. Starostenko, R. Rosas-Romero, J.M. Ramirez-Cortes, and J. Rodriguez-Asomoza, “Wavelet-based smoke detection in outdoor video sequences,” in Proceedings of the 53rd IEEE Midwest Symposium on Circuits and Systems (MWSCAS), pp. 383 –387, 2010.
[7] F. Yuan, “A fast accumulative motion orientation model based on integral image for video smoke detection,” Pattern Recognition Letters, vol. 29, no. 7, pp. 925 - 932, 2008.
[8] B. U. Töreyin, Y. Dedeoglu, and A.E. Çetin, “Wavelet-based real-time smoke detection in video,” in European Signal Processing Conference (EUSIPCO), pp. 246- 302, 2005.
[9] B. U. Töreyin, Y. Dedeoglu, and A.E. Çetin, “Flame Detection in Video Using Hidden Markov Models,” in International Conference on Image Processing (ICIP 2005), pp. 1230–1233, 2005.
[10] B. U. Töreyin, Fire Detection Algorithms Using Multimodal Signal and Image Analysis. PhD thesis, Dept. Elect. Eng., Bilkent University, Ankara, Turkey. Available at: http://www.arehna.di.uoa.gr/thesis/uploaded_data/Fire_Detection_Algorithms_Using_Multimodal_Signal_and_Image_Analysis_2009_thesis_1232106137.pdf, 2009.
[11] P. Piccinini, S. Calderara, and R. Cucchiara, “Reliable smoke detection system in the domains of image energy and colour,” in Proceedings of International Conference on Image Processing, pp. 1376 –1379, 2008.
[12] L. Chen-Yu, L. Chin-Teng, H. Chao-Ting, and S. Miin-Tsair, “Smoke Detection using Spatial and Temporal Analyses,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 6, pp. 200-300, 2012.
[13] J.A. Ojo and J.A. Oladosu, “Video-based Smoke Detection Algorithms: A Chronological Survey,” International Institute for Science, Technology and Education (IISTE), ISSN 2222-1719 (Paper), ISSN 2222-2863 (Online) vol. 5, no. 7, 2014.
[14] B.U. Töreyin, Y. Dedeoglu, A.E. Çetin, S. Fazekas, D. Chetverikov, and T. Amiaz, et al., “Dynamic texture detection, segmentation and analysis,” Proceedings of ACM International Conference on Image and Video Retrieval (CIVR), pp. 131 –134, 2007,
[15] B.U. Töreyin and A. E. Çetin, “Wildfire detection using LMS based active learning,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 12-100, 2009.
[16] U. Ihsan, H. Muhammad, M. Ghulam, A. Hatim, B. George, and M. M. Anwar, “Gender Recognition from Face Images with Local WLD Descriptor,” IWSSIP 2012, Vienna, Austria, vol. 5, no.6, pp. 11-13, 2012.
[17] D. Sloven, P. Renaud, and M. Michel, “Characterization and Recognition of Dynamic Textures based on 2D+T Curvelet Transform,” Signal, Image and Video Processing, pp. 146- 272, 2013.
How to Cite
Ojo, J. A., & Oladosu, J. A. (2018). Effective Smoke Detection Using Spatial-Temporal Energy and Weber Local Descriptors in Three Orthogonal Planes (WLD-TOP). Journal of Computer Science and Technology, 18(01), e05. https://doi.org/https://doi.org/10.24215/16666038.18.e05
Original Articles