Review on Computer Vision Techniques in Emergency Situation

TL;DR

This paper reviews how computer vision techniques are applied across various emergency scenarios, highlighting progress, challenges, and the diversity of approaches in this critical field.

Contribution

It provides a comprehensive survey of computer vision applications in emergencies, categorizing studies by emergency type, objectives, hardware, and algorithms, revealing research overlaps and gaps.

Findings

Wide application of computer vision in emergency detection and response

Diverse hardware and algorithms used across different emergency types

Identification of research overlaps and areas needing further exploration

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.