## 5 Conclusions  
The main goal of the research was to implement an experimental software solution for video analysis. The core of the proposed service was to bring information from videos recorded from a UAV perspective. However, data from static cameras may also be used as input.  
Experimental verification proved the real behavior of the software solution to be successful. The first part of verification included performance evaluation with a focus on the utilization of optimizations such as motion detection, region of interest, etc. We confirmed that the enhancements reduce the hardware requirements. The highest detection stability of YOLOv4 compared to Faster R-CNN ResNet-50 FPN was successfully proven. The proposed simple object tracking algorithm was also evaluated. We conclude that the proposed approach performs as expected with acceptable and above-average results at an accuracy of around 90% in Sample scenario #1 and accuracy from 95% to 100% in Sample scenario #2. Accuracy in the case of the other four scenarios was 100%.  
UAVs can move to various parts of the city and make recordings in a sufficient time. On the contrary, unfavorable wind conditions lead to a significant camera movement, which may distort results. Proposed countermeasures prominently reduce data degradation through their architecture and optimizations.  
As a result of the mobility, there is optimization in the detection process and our own simple tracking algorithm. The overall solution makes the collection of temporal traffic easier and efficient. Currently, in the Slovak Republic, such monitoring is done manually by counting vehicles or using fixed traffic cameras, which require already existing infrastructure.  
The proposed experimental solution can be extended and provide more data from the real environment--for instance, the utilization of neural networks for color detection of cars, car models, or other object categories. We can also remove the background of images that represent detected cars using Mask R-CNN, which aims at instance segmentation. A significant task is the modification of the neural network architecture and subsequent training for adaptation to small objects. Input from the UAVs carried out as a part of this research, included favorable weather conditions, i.e., mostly sunny, with no wind or other external disturbances. Additional improvements directly related to UAVs will cover countermeasures for the disturbances caused by the weather (wind, fog, or rain).  
Conceptualization, O.K. and M.D.; methodology, M.D.; software, M.D.; validation, F.J. and M.M.; formal analysis, F.J. and I.N.; investigation, M.M. and I.N.; resources, M.M.; data curation, F.J.; writing--original draft preparation, M.D. and O.K; writing--review and editing, M.M. and F.J.; visualization, F.J. and I.N.; 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 the support of the Operational Programme Integrated Infrastructure in the 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.