Real Time Video Based Smoke Detection Using Double Optical Flow Estimation

Authors: Anton Stadler, Thorsten Ike

Abstract:

In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos.

Keywords: Low density, optical flow, upward smoke motion, video based smoke detection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126864

References:

[1] S. Verstockt, P. Lambert, R. Van de Walle, B. Merci, and B. Sette, “State of the art in vision-based fire and smoke dectection,” in International Conference on Automatic Fire Detection, 14th, Proceedings, H. Luck and I. Willms, Eds., vol. 2. University of Duisburg-Essen. Department of Communication Systems, 2009, pp. 285–292.
[2] A. E. Çetin, K. Dimitropoulos, B. Gouverneur, N. Grammalidis, O. Günay, Y. H. Habibo˘glu, B. U. Töreyin, and S. Verstockt, “Video fire detection - review,” Digital Signal Processing, vol. 23, no. 6, pp. 1827 – 1843, 2013.
[3] C. Yu, J. Fang, J. Wang, and Y. Zhang, “Video fire smoke detection using motion and color features,” Fire Technology, vol. 46, no. 3, pp. 651–663, 2010.
[4] C. Yu, Z. Mei, and X. Zhang, “A real-time video fire flame and smoke detection algorithm,” Procedia Engineering, vol. 62, no. 0, pp. 891 – 898, 2013.
[5] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2, ser. IJCAI’81. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1981, pp. 674–679.
[6] 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.
[7] O.-B. Alejandro, M.-G. Leonardo, S.-P. Gabriel, T.-M. Karina, and N.-M. Mariko, “Improvement of a video smoke detection based on accumulative motion orientation model,” in Proceedings of the 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, ser. CERMA ’11. Washington, DC, USA: IEEE Computer Society, 2011, pp. 126–130.
[8] I. Kolesov, P. Karasev, A. Tannenbaum, and E. Haber, “Fire and smoke detection in video with optimal mass transport based optical flow and neural networks,” in Image Processing (ICIP), 2010 17th IEEE International Conference on, 2010, pp. 761–764.
[9] C. Zach, T. Pock, and H. Bischof, “A duality based approach for realtime tv-l1 optical flow,” in Proceedings of the 29th DAGM Conference on Pattern Recognition. Berlin, Heidelberg: Springer-Verlag, 2007, pp. 214–223.
[10] J. Sánchez Pérez, E. Meinhardt-Llopis, and G. Facciolo, “Tv-l1 optical flow estimation,” Image Processing On Line, vol. 2013, pp. 137–150, 2013.
[11] (Online). Available: ftp://ftp.cs.rdg.ac.uk/pub/PETS2007/