Single Image Smoke Detection

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• 作者：Hongda Tian (17)
  Wanqing Li (17)
  Philip Ogunbona (17)
  Lei Wang (17)
  
  17. Advanced Multimedia Research Lab ; ICT Research Institute ; School of Computer Science and Software Engineering ; University of Wollongong ; Wollongong ; Australia
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9004
• 期：1
• 页码：87-101
• 全文大小：1,945 KB
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• 作者单位：Computer Vision -- ACCV 2014
• 丛书名：978-3-319-16807-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.