Single Image Smoke Detection

Despite the recent advances in smoke detection from video, detection of smoke from single images is still a challenging problem with both practical and theoretical implications. However, there is hardly any reported research on this topic in the literature. This paper addresses this problem by proposing a novel feature to detect smoke in a single image. An image formation model that expresses an image as a linear combination of smoke and non-smoke (background) components is derived based on the atmospheric scattering models. The separation of the smoke and non-smoke components is formulated as convex optimization that solves a sparse representation problem. Using the separated quasi-smoke and quasi-background components, the feature is constructed as a concatenation of the respective sparse coefficients. Extensive experiments were conducted and the results have shown that the proposed feature significantly outperforms the existing features for smoke detection.

[1]  Mohinder Malhotra.  Single Image Haze Removal Using Dark Channel Prior , 2016 .

[2]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[5]  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.

[6]  Darko Stipanicev,et al.  Visual spatial-context based wildfire smoke sensor , 2012, Machine Vision and Applications.

[7]  Lei Wang,et al.  Smoke Detection in Video: An Image Separation Approach , 2013, International Journal of Computer Vision.

[8]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

[11]  Feiniu Yuan,et al.  A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection , 2012, Pattern Recognit..

[12]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[13]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

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

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

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

[17]  Vincenzo Piuri,et al.  Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  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.

[19]  Lei Wang,et al.  A Novel Video-Based Smoke Detection Method Using Image Separation , 2012, 2012 IEEE International Conference on Multimedia and Expo.

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

[21]  Yue Wang,et al.  Real-time smoke detection using texture and color features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

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

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

[26]  Carlo S. Regazzoni,et al.  Early fire and smoke detection based on colour features and motion analysis , 2012, 2012 19th IEEE International Conference on Image Processing.

[27]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

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

[29]  Artificial Intelligence and Computational Intelligence , 2011, Lecture Notes in Computer Science.

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

[31]  ByoungChul Ko,et al.  Wildfire smoke detection using spatiotemporal bag-of-features of smoke , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[32]  Dani Lischinski,et al.  Deep photo: model-based photograph enhancement and viewing , 2008, SIGGRAPH Asia '08.

[33]  James C. Bezdek,et al.  Convergence of Alternating Optimization , 2003, Neural Parallel Sci. Comput..

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