INTEGRATION OF DRONES FOR INTELLIGENT CROWD COUNTING IN THE SAFE CITY CONCEPT

Proceedings of International Scientific Conference „ALFATECH – Smart Cities and modern technologies“ (pp. 143-147) 

 

AUTOR(I) / AUTHOR(S): Nikola Gligorijević, Danilo Strugarević , Vladimir Čabrić, Marko Račić

 

Download Full Pdf   

DOI:  10.46793/ALFATECHproc25.143G

SAŽETAK / ABSTRACT:

Rapid urbanization and increasing population density in urban areas pose a significant challenge to maintaining security and effective crowd management during mass gatherings, such as public events, protests and emergencies. The „safe city“ concept relies on modern technologies, including drones and artificial intelligence, to improve security, optimize resource allocation and reduce the risks associated with mass gatherings.This paper explores the use of drones, equipped with advanced cameras and object detection algorithms like YOLO and Fast R-CNN, to count people in crowds and analyze their movements in real time. By combining multi-criteria analysis, the algorithms were evaluated according to key criteria, including accuracy, processing speed, robustness to noise, segmentation efficiency and energy efficiency.The results show that the YOLO algorithm is superior in applications that require fast real-time processing, while Fast R-CNN provides higher accuracy in complex scenarios. Integrating drones with these algorithms enables accurate crowd counting and tracking, which contributes to better security and management in modern urban environments.

KLJUČNE REČI / KEYWORDS:

safe city, drones, artificial intelligence, YOLO, Fast R-CNN, crowd counting, multi-criteria analysis

PROJEKAT / ACKNOWLEDGEMENT:

LITERATURA / REFERENCES:

  • Vasilić, M. (2018). Operationalization of the „smart “city concept on the example of Serbia. Sociologija, 60(2), 518537.
  • Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313-323.
  • Ramachandran, A., & Sangaiah, A. K. (2021). A review on object detection in unmanned aerial vehicle surveillance. International Journal of Cognitive Computing in Engineering, 2, 215-228.
  • Quamar, M. M., Al-Ramadan, B., Khan, K., Shafiullah, M., & El Ferik, S. (2023). Advancements and applications of drone-integrated geographic information system technology—A review. Remote Sensing, 15(20), 5039.
  • Rane, N. (2023). YOLO and Faster R-CNN object detection for smart Industry 4.0 and Industry 5.0: applications, challenges, and opportunities. Available at SSRN 4624206.
  • Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
  • Triantaphyllou, E., & Triantaphyllou, E. (2000). Multi-criteria decision making methods (pp. 5-21). Springer Us..
  • Girshick, „Fast R-CNN,“ 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1440-1448, doi: 10.1109/ICCV.2015.169. 
  • https://www.worldbank.org/en/topic/urbandevelopment/overvi ew
  • Stefan Popović, Dejan Đukić, Sonja Đukić Popović, Lazar Kopanja, (2022) Preliminary Research on the Application of Neural Networks to the Combustion Control of Boilers with Automatic Firing, Proceedings of the 8 th Virtual International Conference on Science Technology and Management in Energy, 299 – 303, ISBN-978-86-82602-01-9, COBISS.SR-ID 119331337
  • Pierre Augier, Ashwin Vishnu Mohanan, Cyrille Bonamy1FluidDyn, (april 2019), A Python Open-Source Framework for Research and Teaching in Fluid Dynamics by Simulations, Experiments and Data Processing, Journal of Open Research Software, 7: 9. DOI: https://doi.org/10.5334/jors.237,
  • Stefan Popović; Sonja Djukić Popović; Dejan Djukić; Milan Gligorijević, (2024), Genetic algorithms and machine learning as the basis of all implemented solutions in smart cities, Proceeding of International Scientific and Professional Conference “ALFATECH“Smart Cities and modern technologies March 15, 2024, Belgrade, Serbia, 20 – 20,  ISBN 978-86-6461-074-2, UDK: 711.45:004.7004.85:71, COBISS.SR-ID 14885044, DOI:  10.5281/zenodo.12614935
  • Gligorijević, M., Popović, R., Maksimović, A. The role and importance of integration offunctional telecommunication systems in emergencies, Zbornik radova, Međunarodninaučni skup „Dani Arčibalda Rajsa“, Kriminalističko-policijska akademija, Beograd
  • Stefan Popovic, Dejan Djukic, Sonja Djukic Popovic, Milan Gligorijevic, (2023) Neural networks in pellet combustion control – an overview of the group’s research work in 2022/2023, Procedings of 9th Virtual International Conference on Science, Technology and Management in Energy, Belgrade, Serbia, 249 – 254,  ISBN 978-86-82602-03-3,  COBISS.SR-ID 13926605
  • Bašić, A., Viduka, D., Kraguljac, V., Lavrnić, I., Jevremović, M., Balaban, P., Sajfert, D.,Gligorijević, M., Barzut, S. MultiCriteria Decision Analysis of Wireless Technologies inWPANs for IoT-Enabled Smart Buildings in Tourism. Buildings  2024, 14, 3275. https://doi.org/10.3390/buildings14103275
  • Todorovic, J., Spalevic, P., Panic, S., Milosavljevic, B. & Gligorijevic, M. 2021. FSOsystem performance analysis based on novel Gamma-Chi-square irradiance PDF model. Optica Applicata, 51(3), pp. 335-348.
  • Smith, J., Brown, P., & Taylor, R. (2022). Drones and Smart Cities: Enhancing Public Safety with AI. IEEE Transactions on Smart Cities, 5(3), 220-235.
  • Jones, M., & Wang, L. (2021). Urban Crowd Monitoring Using UAVs and Mobile Computing. Journal of Urban Technology, 28(4), 180-195.
  • Lee, K. (2023). Ethical Considerations in AI-Powered Surveillance Systems. AI & Society, 38(2), 95-112.