A DEEP LEARNING-BAESD EFFICIENT FIREARMS MONITORING TECHNIQUES FOR BUILDING SECURE SMART CITIES
Keywords:
Deep Learning, YOLOv8, DeepSORT, Firearms Detection, Smart Cities, Surveillance Systems, Real-Time Monitoring, Object Tracking, Public Safety, Urban Security.Abstract
The proliferation of firearms in urban environments poses significant challenges to public safety and law enforcement agencies. Traditional surveillance systems often lack the capability to detect and respond to firearm-related incidents in real-time. This paper proposes a deep learning-based approach utilizing the YOLOv8 object detection model and the DeepSORT tracking algorithm to enhance firearms monitoring in smart cities. The system aims to identify and track firearms within surveillance footage, providing timely alerts to law enforcement agencies. Experimental results demonstrate the efficacy of the proposed method in accurately
detecting and tracking firearms, thereby contributing to the development of safer urban environments.
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References
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Improvement. arXiv:1804.02767. arXiv Incremental preprint 2. Bochkovskiy, A., Wang, C.Y., & Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv arXiv:2004.10934.
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