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Single image smoke detection

Conference Paper


Abstract


  • 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.

Publication Date


  • 2015

Citation


  • Tian, H., Li, W., Ogunbona, P. & Wang, L. (2015). Single image smoke detection. In D. Cremers, I. Reid, H. Saito & M. Yang (Eds.), Lecture Notes in Computer Science: Computer Vision - ACCV 2014: 12th Asian Conference on Computer Vision Singapore (pp. 87-101). Switzerland: Springer.

Scopus Eid


  • 2-s2.0-84945945284

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5020

Has Global Citation Frequency


Start Page


  • 87

End Page


  • 101

Place Of Publication


  • Switzerland

Abstract


  • 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.

Publication Date


  • 2015

Citation


  • Tian, H., Li, W., Ogunbona, P. & Wang, L. (2015). Single image smoke detection. In D. Cremers, I. Reid, H. Saito & M. Yang (Eds.), Lecture Notes in Computer Science: Computer Vision - ACCV 2014: 12th Asian Conference on Computer Vision Singapore (pp. 87-101). Switzerland: Springer.

Scopus Eid


  • 2-s2.0-84945945284

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5020

Has Global Citation Frequency


Start Page


  • 87

End Page


  • 101

Place Of Publication


  • Switzerland