VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, this research delves into leveraging AI for enhancing public safety through real-time detection of illegal activities such as robberies and threats at gunpoint using CCTV footage.
With the advancement in deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85\%. Our system stands out by not only spotting robbery and gun point activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in strengthening security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public's sense of safety.