Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering
Article
Article Title | Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering |
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ERA Journal ID | 201448 |
Article Category | Article |
Authors | Perera, Asanka G, Khanam, Fatema-Tuz-Zohra, Al-Naji, Ali and Chahl, Javaan |
Journal Title | Remote Sensing |
Journal Citation | 12 (3) |
Article Number | 577 |
Number of Pages | 22 |
Year | 2020 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2072-4292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/rs12030577 |
Web Address (URL) | https://www.mdpi.com/2072-4292/12/3/577 |
Abstract | 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. |
Keywords | search and rescue; human detection; breathing detection; drone |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4007. Control engineering, mechatronics and robotics |
Byline Affiliations | University of South Australia |
Middle East Technical University, Turkey | |
Defence Science and Technology Group, Australia |
https://research.usq.edu.au/item/z77xy/detection-and-localisation-of-life-signs-from-the-air-using-image-registration-and-spatio-temporal-filtering
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