A new method, named as the nested k‐means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k‐means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red‐green‐blue channels, RGB). First, the k‐means classification is utilized to identify clusters of colors in a three‐dimensional space (RGB). Second, the k‐means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two‐dimensional space (easting‐northing, EN), and has human‐size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k‐means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k‐means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k‐means method may provide the SAR teams with a tool for near real‐time detection of a person and, as a consequence, to reduce search area to approximately 0.5–7.3% of total terrain to be visited, depending on season and land cover.

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This paper reports on the performance of a novel system for supporting search and rescue activities, known as SARUAV (search and rescue unmanned aerial vehicle), in a field experiment during which a real-world search scenario was simulated. The experiment took place on March 2-3, 2017, at two sites located in southwestern Poland. Three groups acted in the experiment: (1) SARUAV and unmanned aerial vehicle (UAV) operators, (2) ground searchers, and (3) participants who simulated being lost. In the uncomplicated topography without snow cover, the system identified the lost persons, and ground searchers found them 31 min after the SARUAV report had been disseminated. In the mountainous area covered with snow, one person was found within 9 min after searchers received the SARUAV report; however, the other two persons were not identified by SARUAV. The field experiment served as a proof of concept of the SARUAV system, confirmed its potential in person identification studies, and helped to identify numerous scientific and technical problems that need to be solved to develop a mature version of the system. K E Y W O R D Slost person, nested k-means, ring model, target detection, unmanned aerial vehicle

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.

The objective of this paper is to investigate the role of clouds in the effectiveness of automated human detection in aerial imagery acquired by unmanned aerial vehicles (UAVs). The automated processing is carried out with the nested k-means method applied to images taken in poor visibility caused by lowaltitude clouds. Data were acquired during a field experiment carried out in the Izerskie Mountains (southwestern Poland). The fixed-wing UAV took RGB aerial photographs of terrain where persons simulated being lost in the wilderness. The UAV flights were conducted in the morning and around the noon, when clouds reduced clarity of aerial images. Subsequent UAV missions were performed in the afternoon and in the evening, when clouds had no impact on imagery. False hit rates ! 50% correspond to clear imagery (8 of 9 non-cloudy cases). In contrast, images impacted by clouds reveal false hit rates 40% (5 of 7 cloudy cases). Sensitivity analysis, carried out on a basis of artificially blurred imagery, confirms that reduced image clarity may improve automated human detection.