International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 786KB)
Geospatial Detection and Movement Analysis System for Unmanned Aerial Vehicles Based on Computer Vision Methods
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Author(s)
Index Terms
UAV Detection, Geospatial Analysis, Trajectory Prediction, Computer Vision, Single-camera Tracking, Height Estimation Algorithm, Mathematical Modeling, Automated Systems, Software Architecture
Abstract
The rapid proliferation of Unmanned Aerial Vehicles (UAVs) across military, commercial, and civilian domains creates unprecedented security challenges while simultaneously offering significant operational advantages. Current detection and tracking systems face mounting pressure to balance effectiveness with deployment complexity and cost constraints. This paper presents a geospatial detection and movement analysis system for Unmanned Aerial Vehicles that addresses critical security challenges through innovative mathematical and software solutions. The research introduces a methodology for UAV monitoring that minimizes sensor requirements, utilizing a single optical sensor equipped with distance measurement capabilities. The core of this work focuses on developing and evaluating an algorithm for three-dimensional (3D) coordinate determination and trajectory prediction without requiring direct altitude measurement. The proposed approach integrates computer vision detection results with a mathematical model that defines spatial relationships between camera parameters and detected objects. Specifically, the algorithm estimates altitude parameters and calculates probable flight trajectories by analyzing the correlation between apparent size variation and measured distance changes across continuous detections. The system implements a complete analytical pipeline, including continuous detection processing, geospatial coordinate transformation, trajectory vector calculation, and visualization on geographic interfaces. Its modular architecture supports real-time analysis of video streams, representing detected trajectories as vector projections with associated uncertainty metrics. The algorithm's capability to provide reliable trajectory predictions is demonstrated through validation in synthetically generated environments. It offers a cost-effective monitoring solution for small aerial objects across diverse environmental conditions. This research contributes to the development of minimally-instrumented UAV tracking systems applicable in both civilian and defense scenarios.
Cite This Paper
Iryna Yurchuk, Danyil-Mykola Obertan, "Geospatial Detection and Movement Analysis System for Unmanned Aerial Vehicles Based on Computer Vision Methods", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.4, pp.16-27, 2025. DOI:10.5815/ijitcs.2025.04.02
Reference
[1]Ekka, P.: A Review Paper on Unmanned Aerial Vehicle (U.A.V.), Int. J. Eng. Res. Tech., vol. 5, no. 23, pp. 1–3 (2017). doi: 10.17577/IJERTCONV5IS23008
[2]Bi, L., Xu, Z.-X., and Yang, L.: Low-cost UAV detection via WiFi traffic analysis and machine learning, Sci. Rep., vol. 13, p. 20892 (2023). https://doi.org/10.1038/s41598-023-47453-6
[3]Kumar, C. A., Giridhar, O.: UAV Detection Multi-sensor Data Fusion. Journal of Research in Science and Engineering, 6(7), 6–12 (2024). https://doi.org/10.53469/jrse.2024.06(07).02
[4]Parrek, A., Singh, V., and Jain, A.: Detection Algorithms for Detecting Drones/UAVs, Darpan Int. Res. Anal., vol. 12, no. 3, pp. 340–351 (2024). http://doi.org/10.36676/dira.v12.i3.93
[5]Zitar, R.A., Al-Betar, M., Ryalat, M., and Kassaymeh, S.: A review of UAV Visual Detection and Tracking Methods, arXiv e-Print Archive (2023). https://doi.org/10.48550/arXiv.2306.05089
[6]Al-Emadi, S., Al-Senaid, F.: Drone Detection Approach Based on Radio-Frequency Using Convolutional Neural Network, IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, 2020, pp. 29-34 (2020). https://doi.org/10.1109/ICIoT48696.2020.9089489
[7]Grichshenko, V., Mukushev, A., Kokidko, A., Zikiryaev, N.: Challenges of Computer Vision for Commercial Unmanned Aerial Vehicle Detection, Journal of Aerospace Technology and Management, 17 (2025). https://doi.org/10.1590/jatm.v17.1377
[8]Rozantsev, A., Lepetit, V., Fua, P.: Flying objects detection from a single moving camera, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 4128-4136 (2015). https://doi.org/10.1109/CVPR.2015.7299040
[9]Amosa, T.I. et al.: Multi-camera multi-object tracking: A review of current trends and future advances, Neurocomputing, vol. 552, p. 126558 (2023). https://doi.org/10.1016/j.neucom.2023.126558
[10]Hu, S., Goldman, G.H., Orel-Donohue, C.C.: Detection of unmanned aerial vehicles using a visible camera system, Appl. Opt. 56, B214-B221 (2017). https://doi.org/10.1364/AO.56.00B214
[11]Gonchigsumlaa, K., Kim, Y., Yeo, K.M., Park, S.H., Lee, Y.T.: Strategic optimal control of multi-camera trajectories for UAV capture using entropy and coverage approaches, BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES, Vol. 73(3), Article number: e153828 (2025). doi: 10.24425/bpasts.2025.153828
[12]Zhang, X., Kusrini, K.: Autonomous long-range drone detection system for critical infrastructure safety, Multimed Tools Appl 80, 23723–23743 (2021). https://doi.org/10.1007/s11042-020-10231-x
[13]Vo Hoai Viet, Huynh Nhat Duy, " Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video", International Journal of Image, Graphics and Signal Processing, Vol.14, No.1, pp. 64-81, 2022.
[14]Sergii Zhdanov, Natalia Kadet, Valerii Silkov, Mariia Zirka, Olexander Rasstrygin, Andrii Zirka, "The Mathematical Model for Research of the UAV Longitudinal Moving", International Journal of Computer Network and Information Security, Vol.13, No.5, pp.29-39, 2021.
[15]Muhammad K. Kabir, Anika N. Binte Kabir, Jahid H. Rony, Jia Uddin, "Drone Detection from Video Streams Using Image Processing Techniques and YOLOv7", International Journal of Image, Graphics and Signal Processing, Vol.16, No.2, pp. 83-95, 2024.