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RSS FeedsRemote Sensing, Vol. 13, Pages 4903: First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland) (Remote Sensing)

 
 

3 december 2021 21:59:23

 
Remote Sensing, Vol. 13, Pages 4903: First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland) (Remote Sensing)
 


Recent advances in search and rescue methods include the use of unmanned aerial vehicles (UAVs), to carry out aerial monitoring of terrains to spot lost individuals. To date, such searches have been conducted by human observers who view UAV-acquired videos or images. Alternatively, lost persons may be detected by automated algorithms. Although some algorithms are implemented in software to support search and rescue activities, no successful rescue case using automated human detectors has been reported on thus far in the scientific literature. This paper presents a report from a search and rescue mission carried out by Bieszczady Mountain Rescue Service near the village of Cergowa in SE Poland, where a 65-year-old man was rescued after being detected via use of SARUAV software. This software uses convolutional neural networks to automatically locate people in close-range nadir aerial images. The missing man, who suffered from Alzheimer’s disease (as well as a stroke the previous day) spent more than 24 h in open terrain. SARUAV software was allocated to support the search, and its task was to process 782 nadir and near-nadir JPG images collected during four photogrammetric flights. After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates (uploading 121 photos from a flight over a lost person; image processing and verification of hits lasted 5 min 48 s). The presented case study proves that the use of an UAV assisted by SARUAV software may quicken the search mission.


 
55 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 13, Pages 4904: Measurements and Modeling of Optical-Equivalent Snow Grain Sizes under Arctic Low-Sun Conditions (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4902: SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation (Remote Sensing)
 
 
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