Victim Detection Using UAV with On-board Voice Recognition System

Yamazaki, Yoshihiro; Tamaki, Masaya; Premachandra, Chinthaka; Perera, Chamika Janith; Sumathipala, Sagara; Sudantha, B. H.

doi:10.1109/irc.2019.00114

Cited by 43 publications

(25 citation statements)

References 12 publications

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“…However, it was limited to detecting human forms over short distances. A drone equipped with a voice recognition system was proposed by Yamazaki et al [21] to detect victims in a disaster stricken area. Nevertheless, all of the studies discussed above were only interested in detecting human forms and their location but they did not consider whether the victim was alive or not, so no life detection was attempted in these studies.…”

Section: Related Workmentioning

confidence: 99%

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

et al. 2020

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In search and rescue operations, it is crucial to rapidly identify those people who are alive from those who are not. If this information is known, emergency teams can prioritize their operations to save more lives. However, in some natural disasters the people may be lying on the ground covered with dust, debris, or ashes making them difficult to detect by video analysis that is tuned to human shapes. We present a novel method to estimate the locations of people from aerial video using image and signal processing designed to detect breathing movements. We have shown that this method can successfully detect clearly visible people and people who are fully occluded by debris. First, the aerial videos were stabilized using the key points of adjacent image frames. Next, the stabilized video was decomposed into tile videos and the temporal frequency bands of interest were motion magnified while the other frequencies were suppressed. Image differencing and temporal filtering were performed on each tile video to detect potential breathing signals. Finally, the detected frequencies were remapped to the image frame creating a life signs map that indicates possible human locations. The proposed method was validated with both aerial and ground recorded videos in a controlled environment. Based on the dataset, the results showed good reliability for aerial videos and no errors for ground recorded videos where the average precision measures for aerial videos and ground recorded videos were 0.913 and 1 respectively.

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Section: Related Workmentioning

confidence: 99%

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

et al. 2020

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“…The comments of the experts refer mainly to this difficulty of drones to detect signs of life. In this regard, we must again recall the work of Al-Naji et al [44] in which they obtain vital signs remotely through different imaging techniques and the study of Yamazaki et al [46] in which they manage to detect victims under the rubble by means of a two-way audio system, capable of capturing the voice response and determining where it is located within the scene.…”

Section: Discussionmentioning

confidence: 99%

Development of the Aerial Remote Triage System: Result of a survey of international experts

Álvarez-García¹,

Cámara-Anguita²,

López-Hens³

et al. 2020

Preprint

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The use of drones for the triage of victims in mass-casualty incidents has recently emerged as a promising technology. However, there is not a triage system really adapted to a remote usage. The objective of our study was to develop a remote triage procedure using drones. The research was performed in three stages: literature review, development of a remote triage algorithm using drones and evaluation of the algorithm by experts. A qualitative synthesis and the calculation of content validity ratios were done to achieve the Aerial Remote Triage System. The system assesses first major bleeding, second walking, third conscious (awake) and fourth sign of life; and classify the victims inside priority categories: priority 1 (red), priority 2 (yellow), priority 3 (green) and priority * (violet). It includes the possibility to indicate save-living interventions to victims and bystanders, like the compression of bleeding injuries or the recovery position. The Aerial Remote Triage System is valid in complex health emergencies when it is difficult or impossible an immediate access to the scene due to physical, chemical or biological risks. It can be useful to know vital information about the emergencies.

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“…In this section, we describe previous research into the two main human search technologies used in the present research, namely, voice processing and image processing. For human search by voice processing, we previously studied sound recording using a unidirectional microphone mounted on a UAV and detecting people from voice information [11]. We tried using digital-filter voice processing to remove only noise from the mixed sound of voices and propellers, but it was difficult to remove only the propeller sound without losing the voice sound.…”

Section: Related Workmentioning

confidence: 99%

“…The measurement was performed with and without the UAV propellers turning, and the detection rates of each case were compared. In addition, the same experiment was performed in an environment in which a single microphone was mounted as the voice input device [11], and the results when voice recognition was performed without performing soundsource separation processing were also measured. For the measurement, we attempted speech recognition 20 times at each distance and checked whether it was possible to detect people.…”

Section: A Experimental Environmentmentioning

confidence: 99%

Audio-Processing-Based Human Detection at Disaster Sites With Unmanned Aerial Vehicle

2020

Self Cite

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This paper describes a human search system that uses an unmanned aerial vehicle (UAV). The use of robots to search for people is expected to become an auxiliary tool for saving lives during a disaster. In particular, because UAVs can collect information from the air, there has been much research into human search using UAVs equipped with cameras. However, the disadvantage of cameras is that they struggle to detect people who are hidden in shadows. To solve this problem, we mounted an array microphone on a UAV and to detect the human voice as a means of finding people that cameras cannot. Also a search method is proposed that combines voice and camera human detection to compensate for their respective shortcomings. The rate and accuracy of human detection by the proposed method are assessed experimentally.

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Victim Detection Using UAV with On-board Voice Recognition System

Cited by 43 publications

References 12 publications

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

Development of the Aerial Remote Triage System: Result of a survey of international experts

Audio-Processing-Based Human Detection at Disaster Sites With Unmanned Aerial Vehicle

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