Abstract
This paper proposes an automatic computer vision-based system for fire detection in videos. There are many previous methods for video-based fire detection but only very few of them have considered the challenge of camera motion or motion of the background scene while finding features based on motion of fire. Our method is divided into two phases. First, we train our system for color characteristics of fire with the help of Gaussian mixture model (GMM), and for texture features which are computed using local binary patterns (LBPs). Next, dense trajectories are computed for motion features which are free from camera motion or challenges of moving scene. Bounding boxes are detected with the help of color and texture models. Subsequently, dense trajectories are projected onto codebooks for feature vector computation, and chi-square kernel-based SVM is employed for classification of fire and non-fire motion representations. Quantitative evaluation of our method indicates the fitness of temporal features for fire detection.
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References
Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. Journal of Visual Communication and Image Representation 18(2), 176–185 (2007)
Chen, J., He, Y., Wang, J.: Multi-feature fusion based fast video flame detection. Building and Environment 45(5), 1113–1122 (2010)
Chen, T.H., Wu, P.H., Chiou, Y.C.: An early fire-detection method based on image processing. In: International Conference on Image Processing, (ICIP’04), vol. 3, pp. 1707–1710. IEEE (2004)
Cui, Y., Dong, H., Zhou, E.: An early fire detection method based on smoke texture analysis and discrimination. In: Congress on Image and Signal Processing (CISP’08), vol. 3, pp. 95–99. IEEE (2008)
Günay, O., Taşdemir, K., Töreyin, B.U., Çetin, A.E.: Fire detection in video using lms based active learning. Fire technology 46(3), 551–577 (2010)
Han, D., Lee, B.: Flame and smoke detection method for early real-time detection of a tunnel fire. Fire Safety Journal 44(7), 951–961 (2009)
Huang, J., Zhao, J., Gao, W., Long, C., Xiong, L., Yuan, Z., Han, S.: Local binary pattern based texture analysis for visual fire recognition. In: 3rd International Congress on Image and Signal Processing (CISP), vol. 4, pp. 1887–1891. IEEE (2010)
Kolesov, I., Karasev, P., Tannenbaum, A., Haber, E.: Fire and smoke detection in video with optimal mass transport based optical flow and neural networks. In: IEEE International Conference on Image Processing (ICIP), pp. 761–764. IEEE (2010)
Liu, Z.G., Yang, Y., Ji, X.H.: Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the ycbcr color space. Signal, Image and Video Processing 10(2), 277–284 (2016)
Ma, L., Wu, K., Zhu, L.: Fire smoke detection in video images using kalman filter and gaussian mixture color model. In: International Conference on Artificial Intelligence and Computational Intelligence (AICI), vol. 1, pp. 484–487. IEEE (2010)
Qi, X., Ebert, J.: A computer vision based method for fire detection in color videos. International journal of imaging 2(S09), 22–34 (2009)
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Action recognition by dense trajectories. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3169–3176. IEEE (2011)
Xu, Z., Xu, J.: Automatic fire smoke detection based on image visual features. In: International Conference on Computational Intelligence and Security Workshops, pp. 316–319. IEEE (2007)
Ye, W., Zhao, J., Wang, S., Wang, Y., Zhang, D., Yuan, Z.: Dynamic texture based smoke detection using surfacelet transform and hmt model. Fire Safety Journal 73, 91–101 (2015)
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Pundir, A.S., Buckchash, H., Rajput, A.S., Tanwar, V.K., Raman, B. (2018). Fire Detection Using Dense Trajectories. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_17
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DOI: https://doi.org/10.1007/978-981-10-7898-9_17
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