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Video Fire Smoke Detection Using Motion and Color Features

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An Erratum to this article was published on 14 January 2010

Abstract

A novel video smoke detection method using both color and motion features is presented. The result of optical flow is assumed to be an approximation of motion field. Background estimation and color-based decision rule are used to determine candidate smoke regions. The Lucas Kanade optical flow algorithm is proposed to calculate the optical flow of candidate regions. And the motion features are calculated from the optical flow results and use to differentiate smoke from some other moving objects. Finally, a back-propagation neural network is used to classify the smoke features from non-fire smoke features. Experiments show that the algorithm is significant for improving the accuracy of video smoke detection and reducing false alarms.

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Authors and Affiliations

  1. State Key Laboratory of Fire Science, USTC, Number 96 Jin Zhai Road, Hefei, Anhui, China

    Yu Chunyu, Fang Jun, Wang Jinjun & Zhang Yongming

Authors
  1. Yu Chunyu
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  2. Fang Jun
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  3. Wang Jinjun
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  4. Zhang Yongming
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Corresponding author

Correspondence to Zhang Yongming.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s10694-010-0138-0

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Chunyu, Y., Jun, F., Jinjun, W. et al. Video Fire Smoke Detection Using Motion and Color Features. Fire Technol 46, 651–663 (2010). https://doi.org/10.1007/s10694-009-0110-z

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  • DOI: https://doi.org/10.1007/s10694-009-0110-z

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