A comprehensive review of AI-powered campus surveillance

Department of CSE(AI&ML), Malnad College of Engineering, Hassan 573202, India

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Abstract

As education institutions face new security challenges, the integration of Artificial intelligence (AI) and computer vision with surveillance systems for real time monitoring and threat detection is becoming mainstream. The inefficiency of a traditional CCTV system, which rely on human monitoring, makes them susceptible to costly mistakes. This literature survey examines deep learning methodologies focused on Convolutional Neural Networks, YOLO based object detection, Haar Cascade classification, and Local Binary Pattern Histogram for campus surveillance and recognition systems used in the 46 works collected between 2020 and 2025. The survey tracks the advancements made towards systems that autonomously monitor and recognize faces, track vehicles, analyse crowds, and even detect behaviours, as AI systems attain the ability to automate processes. Although the systems in question boost recognition accuracy exceeding 95%, real time system flexibility, varying lighting conditions, occlusions, privacy, and system scalability remind touchy problems. The survey suggests that the AI powered systems of the future should work towards smart frameworks that integrate disparate surveillance systems and automated alert systems.