Amneen: An AI-Based System for Real-Time Tracking and Management of Crowds
TL;DR
Amneen is a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV) to track crowd density in public places and prevent dangerous situations before they occur, and predicts congestion before it occurs.
Abstract
With increasing crowd sizes nowadays, the risks of overcrowding, including injuries, accidents, and even fatalities, have become a great concern. Motivated by the need for safer public spaces, this work designed and developed Amneen, a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV). The system provides authorities and event organizers with a real-time tool to track crowd density in public places and prevent dangerous situations before they occur. Amneen integrates two AI models: a head detection model using YOLO-11 and an overcrowding prediction model using Stochastic Gradient Descent Regressor (SGDRegressor). Using live video footage from installed cameras, the system estimates the number of people in a specific area, displays crowd statistics through an interactive dashboard, and sends early warnings when the situation worsens. Additionally, by analyzing historical data patterns, the system predicts congestion before it occurs. The detection model demonstrated strong performance in real-time, processing an image in 6.5 ms with a precision of 93.95%, a recall of 90.91%, an F1-score of 92.41%, and a mean Average Precision (mAP) of 96.26%. The prediction model yielded an MAE of 18.11 and an score of 0.53, indicating moderate predictive performance. Unit and usability testing demonstrated effectiveness and ease of use, highlighting its potential to improve the general quality of life.