Deep Learning Approach For Suspicious Activity Detection from Surveillance Video in Examination Hall

This paper presents a deep learning-based approach for detecting suspicious activities in examination halls using surveillance video. The traditional methods of monitoring students during examinations have limitations, particularly when it comes to real-time detection and alerting. To address this issue, a system is proposed that employs computer vision and deep learning techniques to identify suspicious behaviors such as cheating, unauthorized movements, or distractions. The system processes video feeds from surveillance cameras, analyzes the actions of students, and triggers real-time alerts when abnormal behavior is detected. Experimental results show that the proposed system can detect suspicious activities with high accuracy, providing an automated and reliable mechanism for monitoring exam environments. This paper highlights the potential of deep learning to enhance security and integrity in educational settings