An Artificial Intelligence Mechanism to Monitor and Manage Crowd

Abstract

Effective crowd control is becoming more and more important for everyone's safety and wellbeing due to the growing number of people living in cities and big events. The accuracy and scalability of existing crowd monitoring and management systems are limited by their manual, labor-intensive nature. AI has shown promise in fixing a number of issues. This work presents a real-time artificial intelligence method for monitoring and controlling crowd behavior. YOLO v8 and image processing are used to detect and count crowds using a variety of processes, including picture acquisition, preprocessing, object recognition, and post-processing. Once the crowds have been identified and counted, the results may be evaluated for accuracy, efficacy, and potential applications. In order to monitor and identify people and assess crowds, the suggested system makes use of deep learning, machine learning, and computer vision techniques.