International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Mrs. G. Rohini Phaneendra Kumari, Desineti NagaLakshmi, Gangavarapu Sailaja,, Yaganti Pavani, Nalluri Gayatri Priya
Abstract: The increasing complexity of urban environments and open spaces calls for sophisticated surveillance systems that can identify threats instantly. Human operator-based traditional video monitoring methods are often inefficient, error-prone, and have limited scalability. This paper introduces a smart surveillance system that integrates deep learning, machine learning, and computer vision technologies to detect the occurrence of weapons, violent acts, fire, or smoke in real time.Video streams are locally processed using edge computing which reduces latency and network congestion and, at the same time, allows devices with limited resources to operate efficiently. Compact convolutional neural networks along with object detection algorithms such as YOLOv8 are used to obtain precise classification and tracking, whereas Explainable AI provides interpretability and human trust in automated decisions.Along with adaptive ML-based security solutions to defend the edge devices from cyber-physical attacks, secure data transmission, camera installation optimization, multi-threat detection, and crowd behavior analysis are some of the other features of the system. The experimental evaluation, as conveyed, is accurate, has low latency, and is scalable, thus, the system is suitable for smart cities, transportation hubs, and critical infrastructure. The paper positions AI-powered, edge-enabled surveillance as a way to improve situational awareness and enhance public safety.
