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A Flood Detection and Warning System Based on Video Content Analysis

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

Floods are becoming more frequent and extreme due to climate change. Early detection is critical in providing a timely response to prevent damage to property and life. Previous methods for flood detection make use of specialized sensors or satellite imagery. In this paper, we propose a method for event detection based on video content analysis of feeds from surveillance cameras, which have become more common and readily available. Since these cameras are static, we can use image masks to identify regions of interest in the video where the flood would likely occur. We then perform background subtraction and then use image segmentation on the foreground region. The main features of the segment that we use to identify if it is a flooded region are: color, size and edge density. We use a probabilistic model of the color of the flood based on our set of collected flood images. We determine the size of the segment relative to the frame size as another indicator that it is flood since flooded regions tend to occupy a huge region of the frame. Finally, we perform a form of ripple detection by performing edge detection and using the edge density as a possible indicator for ripples and consequently flood. We then broadcast an SMS message after detecting a flood event consistently across multiple frames for a specified time period. Our results show that this simple technique can adequately detect floods in real-time.

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Author information

Authors and Affiliations

  1. College of Computer Studies, De La Salle University, Manila, Philippines

    Martin Joshua P. San Miguel & Conrado R. Ruiz Jr.

Authors
  1. Martin Joshua P. San Miguel
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  2. Conrado R. Ruiz Jr.
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Corresponding author

Correspondence to Conrado R. Ruiz Jr. .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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San Miguel, M.J.P., Ruiz, C.R. (2016). A Flood Detection and Warning System Based on Video Content Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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