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

Article


Perera, Asanka G, Khanam, Fatema-Tuz-Zohra, Al-Naji, Ali and Chahl, Javaan. 2020. "Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering." Remote Sensing. 12 (3). https://doi.org/10.3390/rs12030577
Article Title

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

ERA Journal ID201448
Article CategoryArticle
AuthorsPerera, Asanka G, Khanam, Fatema-Tuz-Zohra, Al-Naji, Ali and Chahl, Javaan
Journal TitleRemote Sensing
Journal Citation12 (3)
Article Number577
Number of Pages22
Year2020
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2072-4292
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.

Keywordssearch and rescue; human detection; breathing detection; drone
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204007. Control engineering, mechatronics and robotics
Byline AffiliationsUniversity of South Australia
Middle East Technical University, Turkey
Defence Science and Technology Group, Australia
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