Smoke Detection in Video: An Image Separation Approach

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.

[1]  Changshui Zhang,et al.  Blindly separating mixtures of multiple layers with spatial shifts , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Laurenz Wiskott,et al.  Slow feature analysis and decorrelation filtering for separating correlated sources , 2011, 2011 International Conference on Computer Vision.

[3]  Ronen Basri,et al.  Separation of Transparent Layers using Focus , 2004, International Journal of Computer Vision.

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

[5]  S. Osher,et al.  IMAGE DECOMPOSITION AND RESTORATION USING TOTAL VARIATION MINIMIZATION AND THE H−1 NORM∗ , 2002 .

[6]  Liangfeng Guo,et al.  The use of entropy minimization for the solution of blind source separation problems in image analysis , 2006, Pattern Recognit..

[7]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

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

[9]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[10]  Ronald R. Coifman,et al.  Multilayered image representation: application to image compression , 2002, IEEE Trans. Image Process..

[11]  Simone Calderara,et al.  Vision based smoke detection system using image energy and color information , 2011, Machine Vision and Applications.

[12]  Jong-Myon Kim,et al.  An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems , 2011 .

[13]  Michal Irani,et al.  Separating transparent layers of repetitive dynamic behaviors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[15]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[16]  Imari Sato,et al.  Separating reflective and fluorescent components of an image , 2011, CVPR 2011.

[17]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[18]  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.

[19]  Michael F. Cohen,et al.  An iterative optimization approach for unified image segmentation and matting , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[20]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[21]  Allen Tannenbaum,et al.  Fire and smoke detection in video with optimal mass transport based optical flow and neural networks , 2010, 2010 IEEE International Conference on Image Processing.

[22]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[23]  Guillermo Sapiro,et al.  Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting , 2009, International Journal of Computer Vision.

[24]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, ECCV.

[27]  Fujio Kurokawa,et al.  Image based smoke detection with local Hurst exponent , 2010, 2010 IEEE International Conference on Image Processing.

[28]  Mohamed-Jalal Fadili,et al.  Image Decomposition and Separation Using Sparse Representations: An Overview , 2010, Proceedings of the IEEE.

[29]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[30]  Richard Szeliski,et al.  Layer extraction from multiple images containing reflections and transparency , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[31]  Jing Huang,et al.  Transmission: A New Feature for Computer Vision Based Smoke Detection , 2010, AICI.

[32]  Edward H. Adelson,et al.  Separating reflections and lighting using independent components analysis , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[33]  Wanqing Li,et al.  Smoke detection in videos using Non-Redundant Local Binary Pattern-based features , 2011, 2011 IEEE 13th International Workshop on Multimedia Signal Processing.

[34]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Luís B. Almeida,et al.  Separating a Real-Life Nonlinear Image Mixture , 2005, J. Mach. Learn. Res..

[36]  Sung Yong Shin,et al.  High-Quality Reflection Separation Using Polarized Images , 2011, IEEE Transactions on Image Processing.

[37]  Zhang Yongming,et al.  Video Fire Smoke Detection Using Motion and Color Features , 2010 .

[38]  Yannick Deville,et al.  Maximum Likelihood Blind Image Separation Using Nonsymmetrical Half-Plane Markov Random Fields , 2009, IEEE Transactions on Image Processing.

[39]  Stanley Osher,et al.  Image Decomposition and Restoration Using Total Variation Minimization and the H1 , 2003, Multiscale Model. Simul..

[40]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[41]  Anna Tonazzini,et al.  A Markov model for blind image separation by a mean-field EM algorithm , 2006, IEEE Transactions on Image Processing.

[42]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Erkki Oja,et al.  Independent component analysis for artefact separation in astrophysical images , 2003, Neural Networks.

[44]  Assaf Zomet,et al.  Separating reflections from a single image using local features , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..