Efficient Aggressive Behaviour Detection And Alert System Employing Deep Learning Techniques

Effective surveillance systems are more important than ever in a world where violent crimes constantly threaten social stability and individual safety. While static monitoring has been the mainstay of traditional systems, real-time, dynamic detection and reaction is the way of the future. In order to create a real-time Violence and Weapon Detection Alert System, this study presents a comprehensive system that combines cutting-edge computer vision techniques with the strength of cloud-based alerts. A series of steps for improved monitoring are included in the suggested approach. The first steps involve detecting whether or not human beings are present in video frames, effectively removing unnecessary content, and minimizing computational cost. The frames are then put via a dual analysis process. Pre trained deep learning algorithm MobileNetv2 recognize violence, while YOLOv8 recognizes the presence of weapons. The method overcomes the drawbacks of laborious calculation while attaining high accuracy by utilizing the strengths of YOLOv8 for weapon identification and MobileNetv2 for violence detection. The system's performance is maximized by the parallel processing of both models, guaranteeing that security staff can quickly evaluate and address problems. In a time of growing security concerns, the suggested Efficient Aggressive Behaviour Detection and Alert System provides a cutting-edge way to improve security, promote a safer atmosphere, and support societal stability. Real-time violence and weapon detection integrated together is a critical first step towards all-encompassing surveillance and quick reaction, protecting people and communities alike.