TY - JOUR
T1 - Review on computer vision techniques in emergency situations
AU - Lopez-Fuentes, Laura
AU - van de Weijer, Joost
AU - González-Hidalgo, Manuel
AU - Skinnemoen, Harald
AU - Bagdanov, Andrew D.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - © 2017, Springer Science+Business Media, LLC. 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.
AB - © 2017, Springer Science+Business Media, LLC. 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.
KW - Computer vision
KW - Critical situation
KW - Decision makers
KW - Emergency management
KW - Situational awareness
U2 - 10.1007/s11042-017-5276-7
DO - 10.1007/s11042-017-5276-7
M3 - Article
SN - 1380-7501
VL - 77
SP - 17069
EP - 17107
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 13
ER -