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Open AccessArticle

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

1
School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
2
Electrical Engineering Technical College, Middle Technical University, Baghdad 1022, Iraq
3
Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, VIC 3207, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 577; https://doi.org/10.3390/rs12030577
Received: 14 December 2019 / Revised: 27 January 2020 / Accepted: 4 February 2020 / Published: 9 February 2020
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: search and rescue; drone; breathing detection; human detection search and rescue; drone; breathing detection; human detection
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Perera, A.G.; Khanam, F.-T.-Z.; Al-Naji, A.; Chahl, J. Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering. Remote Sens. 2020, 12, 577.

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