Next Article in Journal
Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China
Previous Article in Journal
Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method
Open AccessArticle

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

School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
Electrical Engineering Technical College, Middle Technical University, Baghdad 1022, Iraq
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;
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop