Deep Learning Approach for Suspicious Activity Detection from Surveillance Video in Exam Hall

Maintaining the integrity of examinations is essential in educational settings. Conventional surveillance is typically based on manual observation, which is inefficient and error- prone. The project introduces a system based on deep learning intended to identify abnormal behavior in examination rooms using automatic, real-time video analysis. By utilizing state-of-theart computer vision technology, the system analyzes continuous video streams, derives behavioral features, and labels activities as normal or abnormal. It covers behavior like unauthorized activities, communication, and object transfer that could imply academic dishonesty. The system incorporates real-time alerting facilities—such as buzzers and immediate notifications—to facilitate prompt intervention by authorities. This not only minimizes the requirement for constant human monitoring but also provides a more objective and scalable monitoring process. With flexibility and cross-platform capabilities, the solution is accessible on low-cost hardware and can be utilized across different environments. Aside from monitoring examinations, the technology can be modified for wider security use in public areas, business premises, and medical facilities. Future development, including multi- camera support, predictive analytics, and privacy-enhancing features, will further enhance its potential. This project illustrates how artificial intelligence can revolutionize surveillance systems to become proactive tools that improve fairness, security, and trust in high-stakes environments