This project involves enhancing traditional object detection methods by incorporating human pose estimation to identify weapons in various contexts, especially in surveillance and security applications. This approach leverages computer vision techniques that analyse the positions and movements of individuals, allowing systems to recognize not just the presence of weapons but also the intent and behaviour of the person carrying them. By integrating pose data with advanced machine learning methods, the system can more accurately recognise threatening situations, distinguishing between benign gestures and potential threats. This analysis improves detection accuracy in complex environments, such as crowded public spaces. The project may cover different types of weapons, but it will primarily focus on two major ones including guns and knives.