Review on computer vision techniques in emergency situations

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....

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2018-07, Vol.77 (13), p.17069-17107
Main Authors: Lopez-Fuentes, Laura, van de Weijer, Joost, González-Hidalgo, Manuel, Skinnemoen, Harald, Bagdanov, Andrew D.
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Closed circuit television
Computer Communication Networks
Computer Science
Computer vision
Data management
Data Structures and Information Theory
Disasters
Emergencies
Hazards
Multimedia Information Systems
Situational awareness
Smartphones
Special Purpose and Application-Based Systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-5276-7