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International Journal of Computer Vision

Erschienen in:

01.01.2014

Smoke Detection in Video: An Image Separation Approach

verfasst von: Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2014

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Abstract

Existing video-based smoke detection methods often rely on the visual features extracted directly from the original frames. In the case of light smoke, the background is still visible and it deteriorates the quality of the features. This paper presents an approach to separating the smoke component from the background such that visual features can be extracted from the smoke component for reliable smoke detection. Specifically, an image is assumed to be a linear blending of a smoke component and a background image. Given a video frame and its background, the estimation of the blending parameter and the actual smoke component can be formulated as an optimization problem. Three methods based on different models for the smoke component are proposed to solve the optimization problem. Experimental results on synthesized and real video data have shown that the proposed approach can effectively separate the smoke component and the smoke detection performance is significantly improved by using the visual features extracted from the smoke component.

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Notice that in Narasimhan and Nayar (2002), the attenuating medium, such as fog, is assumed to occupy the entire space between the scene and the camera. For smoke, this is usually not the case. Smoke often appears at a certain distance and is of limited thickness along the line of sight.

Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322.CrossRef

Almeida, L. B. (2005). Separating a real-life nonlinear image mixture. Journal of Machine Learning Research, 6, 1199–1229.MATHMathSciNet

Bai, X., & Sapiro, G. (2009). Geodesic matting: A framework for fast interactive image and video segmentation and matting. International Journal of Computer Vision, 82(2), 113–132.CrossRef

Bell, A. J., & Sejnowski, T. J. (1995). An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.CrossRef

Calderara, S., Piccinini, P., & Cucchiara, R. (2011). Vision based smoke detection system using image energy and color information. Machine Vision and Applications, 22(4), 705–719.CrossRef

Chen, T., Yin, Y., Huang, S., & Ye, Y. (2006). The smoke detection for early fire-alarming system base on video processing. In Proceedings of international conference on intelligent information hiding and multimedia signal processing (pp. 427–430).

Fadili, M. J., Starck, J. L., Bobin, J., & Moudden, Y. (2010). Image decomposition and separation using sparse representations: An overview. Proceedings of the IEEE, 98(6), 983–994.CrossRef

Farid, H., & Adelson, E. H. (1999). Separating reflections and lighting using independent components analysis. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 262–267.

Funaro, M., Oja, E., & Valpola, H. (2003). Independent component analysis for artefact separation in astrophysical images. Neural Networks, 16(3–4), 469–478.CrossRef

Gai, K., Shi, Z., & Zhang, C. (2008). Blindly separating mixtures of multiple layers with spatial shifts. In Proceedings of IEEE Computer Society conference on computer vision and pattern recognition (pp. 15–22).

Gonzalez, R. C., & Woods, R. E. (2007). Digital image processing (3rd ed.). Upper Saddle River, NJ: Prentice Hall.

Guidara, R., Hosseini, S., & Deville, Y. (2009). Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields. IEEE Transactions on Image Processing, 18(11), 2435–2450.CrossRefMathSciNet

Guo, L., & Garland, M. (2006). The use of entropy minimization for the solution of blind source separation problems in image analysis. Pattern Recognition, 39(6), 1066–1073.CrossRefMATH

He, K., Sun, J., & Tang, X. (2011). Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341–2353.CrossRef

Kolesov, I., Karasev, P., Tannenbaum, A., & Haber, E. (2010). Fire and smoke detection in video with optimal mass transport based optical flow and neural networks. In Proceedings of IEEE international conference on image processing (pp. 761–764).

Kong, N., Tai, Y. W., & Shin, S. Y. (2011). High-quality reflection separation using polarized images. IEEE Transactions on Image Processing, 20(12), 3393–3405.CrossRefMathSciNet

Lee, H., Battle, A., Raina, R., & Ng, A. Y. (2007). Efficient sparse coding algorithms. In Proceedings of advances in neural information processing Systems.

Levin, A., Lischinski, D., & Weiss, Y. (2008). A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 228–242.CrossRef

Levin, A., Zomet, A., & Weiss, Y. (2004). Separating reflections from a single image using local features. In Proceedings of IEEE Computer Society conference on computer vision and pattern recognition (Vol. 1, pp. 306–313).

Levin, A., & Weiss, Y. (2007). User assisted separation of reflections from a single image using a sparsity prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1647–1655.CrossRef

Long, C., Zhao, J., Han, S., Xiong, L., Yuan, Z., Huang, J., et al. (2010). Transmission: A new feature for computer vision based smoke detection. Lecture notes in artificial intelligence (Vol. 6319, pp. 389–396). Berlin: Springer.

Maruta, H., Nakamura, A., Yamamichi, T., & Kurokawa, F. (2010). Image based smoke detection with local hurst exponent. In Proceedings of IEEE international conference on image processing (pp. 4653–4656).

Meyer, F. G., Averbuch, A. Z., & Coifman, R. R. (2002). Multilayered image representation: Application to image compression. IEEE Transactions on Image Processing, 11(9), 1072–1080.

Minh, H. Q., & Wiskott, L. (2011). Slow feature analysis and decorrelation filtering for separating correlated sources. In Proceedings of IEEE international conference on computer vision (pp. 866–873).

Narasimhan, S. G., & Nayar, S. K. (2002). Vision and the atmosphere. International Journal of Computer Vision, 48(3), 233–254.

Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

Osher, S., Sole, A., & Vese, L. (2003). Image decomposition and restoration using total variation minimization and the \({H}^{-1}\) norm. Multiscale Modeling & Simulation, 1(3), 349–370.CrossRefMATHMathSciNet

Sarel, B., & Irani, M. (2005). Separating transparent layers of repetitive dynamic behaviors. In Proceedings of IEEE international conference on computer vision (Vol. 1, pp. 26–32).

Schechner, Y. Y., Kiryati, N., & Basri, R. (2000). Separation of transparent layers using focus. International Journal of Computer Vision, 39(1), 25–39.CrossRefMATH

Starck, J. L., Elad, M., & Donoho, D. L. (2005). Image decomposition via the combination of sparse representations and a variational approach. IEEE Transactions on Image Processing, 14(10), 1570–1582.CrossRefMathSciNet

Stauffer, C., & Grimson, W. E. L. (2000). Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 747–757.CrossRef

Szeliski, R., Avidan, S., & Anandan, P. (2000). Layer extraction from multiple images containing reflections and transparency. In Proceeding of IEEE Computer Society conference on computer vision and pattern recognition (Vol. 1, pp. 246–253).

Tian, H., Li, W., Ogunbona, P., Nguyen, D. T., & Zhan, C. (2011). Smoke detection in videos using non-redundant local binary pattern-based features. In Proceedings of IEEE international workshop on multimedia signal processing (pp. 1–4).

Tonazzini, A., Bedini, L., & Salerno, E. (2006). A markov model for blind image separation by a mean-field EM algorithm. IEEE Transactions on Image Processing, 15(2), 473–482.CrossRefMathSciNet

Toreyin, B. U., Dedeoglu, Y., & Cetin, A. E. (2005). Wavelet based real-time smoke detection in video. In Proceedings of European signal processing conference.

Tung, T. X., & Kim, J. M. (2011). An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems. Fire Safety Journal, 46(5), 276–282.CrossRef

Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71–86.CrossRef

Wang, J., & Cohen, M. F. (2005). An iterative optimization approach for unified image segmentation and matting. In Proceedings of IEEE International Conference on Computer Vision (pp. 936–943).

Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T. S., & Yan, S. (2010). Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6), 1031–1044.CrossRef

Yu, C., Fang, J., Wang, J., & Zhang, Y. (2010). Video fire smoke detection using motion and color features. Fire Technology, 46(3), 651–663.CrossRef

Yuan, F. (2008). A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recognition Letters, 29(7), 925–932.CrossRef

Zhang, C., & Sato, I. (2011). Separating reflective and fluorescent components of an image. In Proceedings of IEEE Computer Society conference on computer vision and pattern recognition (pp. 185–192).

Titel: Smoke Detection in Video: An Image Separation Approach
verfasst von: Hongda Tian
Wanqing Li
Lei Wang
Philip Ogunbona
Publikationsdatum: 01.01.2014
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2014
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-013-0656-6

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