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Single Image Smoke Detection

  • Hongda TianEmail author
  • Wanqing Li
  • Philip Ogunbona
  • Lei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

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.

Keywords

Support Vector Machine Sparse Representation Local Binary Pattern Block Image Light Smoke 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was partly supported by SNS Unicorp Pty Ltd.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hongda Tian
    • 1
    Email author
  • Wanqing Li
    • 1
  • Philip Ogunbona
    • 1
  • Lei Wang
    • 1
  1. 1.Advanced Multimedia Research Lab, ICT Research Institute, School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia

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