Life Signs Detector Using a Drone in Disaster Zones
In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually hampered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who...
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MDPI AG
2019-10-01
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Online Access: | https://www.mdpi.com/2072-4292/11/20/2441 |
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doaj-5d050b5d643948428c9f11aa0f69cc692020-11-25T01:33:28ZengMDPI AGRemote Sensing2072-42922019-10-011120244110.3390/rs11202441rs11202441Life Signs Detector Using a Drone in Disaster ZonesAli Al-Naji0Asanka G. Perera1Saleem Latteef Mohammed2Javaan Chahl3Electrical Engineering Technical College, Middle Technical University, Baghdad 1022, IraqSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaElectrical Engineering Technical College, Middle Technical University, Baghdad 1022, IraqSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaIn the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually hampered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors.https://www.mdpi.com/2072-4292/11/20/2441cardiopulmonary motionmotion detectiondroneuavopenposedenoisingwavelet |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Al-Naji Asanka G. Perera Saleem Latteef Mohammed Javaan Chahl |
spellingShingle |
Ali Al-Naji Asanka G. Perera Saleem Latteef Mohammed Javaan Chahl Life Signs Detector Using a Drone in Disaster Zones Remote Sensing cardiopulmonary motion motion detection drone uav openpose denoising wavelet |
author_facet |
Ali Al-Naji Asanka G. Perera Saleem Latteef Mohammed Javaan Chahl |
author_sort |
Ali Al-Naji |
title |
Life Signs Detector Using a Drone in Disaster Zones |
title_short |
Life Signs Detector Using a Drone in Disaster Zones |
title_full |
Life Signs Detector Using a Drone in Disaster Zones |
title_fullStr |
Life Signs Detector Using a Drone in Disaster Zones |
title_full_unstemmed |
Life Signs Detector Using a Drone in Disaster Zones |
title_sort |
life signs detector using a drone in disaster zones |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-10-01 |
description |
In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually hampered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors. |
topic |
cardiopulmonary motion motion detection drone uav openpose denoising wavelet |
url |
https://www.mdpi.com/2072-4292/11/20/2441 |
work_keys_str_mv |
AT alialnaji lifesignsdetectorusingadroneindisasterzones AT asankagperera lifesignsdetectorusingadroneindisasterzones AT saleemlatteefmohammed lifesignsdetectorusingadroneindisasterzones AT javaanchahl lifesignsdetectorusingadroneindisasterzones |
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1725077022205542400 |