(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
This paper proposes an AI-based framework for detecting suspicious activities and criminal faces, which can be used to enhance the capabilities of contemporary surveillance systems. The framework combines the power of advanced computer vision and deep learning algorithms, which can be used to carry out real-time surveillance in public as well as high-security zones. Object detection algorithms, specifically the YOLO algorithm, can be used to identify potential threats, which may include weapons and suspicious objects. Similarly, pose estimation and anomaly detection algorithms can be used to identify abnormal human behaviour , which may include aggressiveness and prolonged loitering. To carry out accurate facial verification, a convolutional neural network based on the Grassmannian algorithm can be used, which can accurately match facial features even in adverse lighting and occlusion. A database can also be created to accurately match criminal faces. The integration of automated threat detection, behavioural analysis, and facial recognition enhances situational awareness and reduces dependency on manual surveillance. This research contributes to the development of intelligent security solutions for smart cities, banking systems, and law enforcement applications, improving response time and overall public safety