Review on computer vision techniques in emergency situations

  • Laura Lopez-Fuentes
  • Joost van de Weijer
  • Manuel González-Hidalgo
  • Harald Skinnemoen
  • Andrew D. Bagdanov
Article

Abstract

In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.

Keywords

Emergency management Computer vision Decision makers Situational awareness Critical situation 

Notes

Acknowledgments

This work was partially supported by the Spanish Grants TIN2016-75404-P AEI/FEDER, UE, TIN2014-52072-P, TIN2013-42795-P and the European Commission H2020 I-REACT project no. 700256. Laura Lopez-Fuentes benefits from the NAERINGSPHD fellowship of the Norwegian Research Council under the collaboration agreement Ref.3114 with the UIB. We thank the NVIDIA Corporation for support in the form of GPU hardware.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.TM-RCS Working GroupAnsuR Technologies ASFornebuNorway
  2. 2.Department of Mathematics and Computer ScienceUniversity of the Balearic IslandsPalmaSpain
  3. 3.Computer Vision CenterUniversitat Autònoma de BarcelonaBarcelonaSpain
  4. 4.University of Florence, DINFOFlorenceItaly
  5. 5.Balearic Islands Health Research Institute (IdISBa)PalmaSpain

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