# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 7 Conclusions  
The research focused on the detection of groups of pedestrians with subsequent estimations of their geolocations and cluster analyses performed on the geolocation results. The YOLOv3 algorithm was used for the detection of pedestrians. We conclude that the highest efficiency was achieved with the YOLO algorithm having an input blob resolution of 1248 \(\times\) 1248. The geolocations of pedestrians were based on the original authors' solution for calculating the real geographical location of pedestrians from a video recording. Coordinates of the pedestrians yielded deviations up to 2 m. The solution uses K-means, DBSCAN, and OPTICS clustering algorithms. All algorithms were able to correctly and efficiently visualize groups of people on the map. However, for the highly irregular shapes of data, DBSCAN and OPTICS provide better alternatives.  
The main contribution of the research is the possibility to extract the geolocations of pedestrians and determine the formation of groups. The system is robust for UAV flight, at an altitude of about 30 m above the ground, with a minimum video resolution of 1080p. Due to the separation of the system (into a user web application and a processing program), it is also possible to transfer processing to different devices. The whole system is composed of two separate parts. The first is a Python application, which is responsible for detecting pedestrians from video recordings and determining their real geographical locations. Due to its implementation in Python, the program can run on a wide variety of hardware, e.g., UAVs or desktop computers. The output of this part of the system is a data file with the geographical coordinates of pedestrians at the time of detection. This file is then used as input to the second program, implemented as a web application in ASP.NET. This application is responsible for storing the obtained data in a database, filtering, and selecting data for analysis, as well as selecting clustering algorithms.  
Due to the use of a highly efficient YOLOv3 algorithm, object detection can be performed on a vast variety of hardware, e.g., desktops, UAVs, etc. Further improvements in robustness, analysis tools, and additional terrain testing may be considered. YOLOv5 or newer versions may be utilized to further improve detection accuracy.  
Conceptualization, O.K. and M.G.; methodology, M.M.; software, M.G.; validation, F.J. and M.M.; formal analysis, F.J.; investigation, O.K.; resources, F.J.; data curation, M.G.; writing--original draft preparation, O.K. and M.G.; writing--review and editing, M.M. and F.J.; visualization, M.M.; supervision, F.J.; project administration, O.K.; funding acquisition, F.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This publication was realized with support from the Operational Programme Integrated Infrastructure in frame of the project: Intelligent systems for UAV real-time operation and data processing, code ITMS2014+: 313011V422, and co-financed by the European Regional Development Fund.  
The authors declare no conflict of interest.