Enhancing Public Safety through Real-time Video Surveillance Analytics Using AI and Computer Vision

Author(s)	Ravikanth Konda
Country	United States
Abstract	Recent developments in artificial intelligence (AI) and computer vision have revolutionized the domain of video surveillance. Intelligent surveillance platforms are rapidly replacing legacy monitoring systems based on human monitoring with real-time threat detection, pattern recognition, and behavioural analysis. This paper examines how AI-powered video analytics can be used to improve public safety by facilitating proactive action against criminal and dangerous activity. A detailed survey of deep learning methods—more specifically CNNs, LSTM networks, and object detection models such as YOLOv5—is provided. The article describes an end-to-end system architecture incorporating video input, feature extraction, real-time detection, and automated alert mechanisms. Experimental assessments employing public datasets yield significant enhancements in detection accuracy and response time. The topic also addresses privacy issues, scalability, and ethical deployment options. The research reiterates how AI can fundamentally transform the capability of surveillance systems to become wiser, efficient, and adaptive.
Field	Engineering
Published In	Volume 10, Issue 4, October-December 2019
Published On	2019-12-06
DOI	https://doi.org/10.71097/IJSAT.v10.i4.4681
Short DOI	https://doi.org/g9hjdm

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International Journal on Science and Technology