Real Time Computer Vision Based Crowd Control and Crime Detection Using Deep Learning

In recent times, India has had some noteworthy incidents involving crowds. Nearly 70 percent of crowd-related incidents in India occur around religious holidays, according to statistics. Large crowds need particular vigilance since failing to observe crowd safety protocols might result in a devastating humanitarian situation. Manual crowd control is not only labor-intensive and heavily dependent on human involvement, but it is also more prone to human mistake. This project aims to provide a comprehensive solution for crowd control and preventing crime by integrating AI and ML technologies with existing CCTV networks. The main objective is to develop and implement better algorithms like YOLOv7 that can analyze video feeds in real-time, identifying and assessing crowd dynamics, spotting any criminal activity early, and continuously monitoring work environments. This program highlights how smart video analytics may significantly improve safety and operational efficiency while reducing the need for significant device overhauls, thereby revolutionizing current infrastructure. This model gives better accuracy than other models around 95 percent which this model more efficient than others.