Smart Campus Surveillance and Guidance System
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Abstract
The rapid growth of educational campuses has increased the need for intelligent systems to ensure security, automate attendance, and assist navigation. This paper presents a Smart Campus Surveillance and Guidance System that integrates Artificial Intelligence, Computer Vision, and Machine Learning techniques to enhance campus management. The system utilizes CCTV video streams to perform face recognition-based attendance, real-time anomaly detection, and indoor navigation assistance.
The proposed solution uses deep learning models such as FaceNet for facial recognition and YOLO for object detection. Experimental results show improved accuracy in attendance marking and effective detection of suspicious activities. The system is scalable, cost-effective, and improves overall campus safety and efficiency.
