A Deep Learning Based Real-Time Campus Student Movement Detection System

Dr. G. Sita Ratnam,
B. Ramya, K. Deelep Kumar, G. Sai Kumar, K. Sravani, P. Narasimha Naidu
Department of Computer Science and Engineering
Visakha Institute of Engineering and Technology, Visakhapatnam, Andhra Pradesh, India

Abstract:The project titled “A Deep Learning Based Real-Time Campus Student Movement Detection System” presents an automated and intelligent solution for monitoring and tracking student activities within a campus environment. The system utilizes computer vision and deep learning techniques to detect and recognize student faces in real-time using live video feeds from surveillance cameras. Each video frame is processed to identify faces using a lightweight detection model, and recognized using a deep learning-based feature extraction model. The recognized faces are compared with pre-stored student data in a database, allowing the system to accurately identify individuals and track their movement across different locations. The system is implemented using Python, integrating OpenCV for image processing, TensorFlow Lite for efficient model execution, and Flask for developing a web-based interface for administrators. This approach significantly reduces manual monitoring efforts and enhances campus security by providing continuous and real-time tracking. The system is designed to be efficient, scalable, and user-friendly. Although performance may vary with lighting conditions and camera quality, it provides reliable results under standard conditions. Future enhancements may include multi-camera integration, cloud-based storage, and advanced behavioural analysis. Keywords: Deep Learning, Computer Vision, Face Detection, Face Recognition, Student Tracking, Real-TimeMonitoring, TensorFlow Lite, OpenCV, Flask, Campus Security.