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Fire Detection Model Using Deep Learning Techniques

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

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Abstract

Frame-based and motion-based deep learning framework have been employed in this paper for fire detection. This paper presents a novel method for fire detection that employs different Convolutional Neural Networks (CNN) architectures for fire detection. Firstly, frame based features such as fire-color, fire texture, and analysis of perimeter disorder which are present in the still images were extracted using transfer learning. Secondly, motion-based CNN is used for extracting motion based features of fire such as growing region, moving area and uprising part detection. Optical flow was employed to calculate the motion of frame intensities. These extracted intensity features which being projected as image were fed into Deep CNN for find out the uniqueness in the motion of fire. Features from both the models are combined to feed into different classifiers. Method is tested on different varieties of non-fire videos which are similar to forest fire such as fire-works, sun based videos, traffic at night and flower-valley videos. Accuracy on such similar situations has proven the precision and robustness in the proposed method.

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Notes

  1. 1.

    http://cvpr.kmu.ac.kr/.

  2. 2.

    http://www.openvisor.org.

  3. 3.

    http://signal.ee.bilkent.edu.tr/VisiFire/Demo.

  4. 4.

    https://www.shutterstock.com/video/search/fire.

  5. 5.

    https://www.youtube.com/results?search_query=forest+fire.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India

    Arun Singh Pundir & Balasubramanian Raman

Authors
  1. Arun Singh Pundir
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  2. Balasubramanian Raman
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Corresponding author

Correspondence to Arun Singh Pundir .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Pundir, A.S., Raman, B. (2022). Fire Detection Model Using Deep Learning Techniques. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_34

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  • DOI: https://doi.org/10.1007/978-3-031-11349-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11348-2

  • Online ISBN: 978-3-031-11349-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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