Automated Human Detection and Overcrowding Prediction Using Deep Learning and Django

Abstract

Effective crowd monitoring is essential for public safety, space management, and preventing overcrowding-related risks. This project introduces an Automated Human Detection and Overcrowding Prediction System using Django and deep learning-based computer vision to provide a scalable and efficient solution for real-time crowd monitoring.
The system processes both uploaded video files and live surveil- lance feeds, detects individuals in each frame, and generates alerts if the human count surpasses a predefined threshold, indicating potential overcrowding. To enhance usability, the system supports real-time monitoring with dynamic visualization and automated notifications via email or SMS when overcrowding is detected.
The core functionality relies on an advanced object detec- tion model implemented in HumanDetection.py, which extracts frames from videos, identifies people, and accurately counts them. The Django-based backend manages video processing, real-time data storage, a RESTful API for seamless integration with external applications, and an intuitive web dashboard. The frontend offers interactive heatmaps, crowd density analytics, and historical trend reports to help administrators make informed decisions.
This system has broad applications in public safety, event man- agement, workplaces, transportation hubs, and smart city initia- tives. Future enhancements may include IoT-based automated crowd control, anomaly detection for unusual crowd behavior, AI-powered predictive analytics for crowd flow forecasting, and cloud-based deployment for large-scale accessibility. Additionally, integrating facial recognition (if permitted by privacy regula- tions) or posture analysis could improve security by identifying suspicious activities.
By leveraging deep learning, real-time analytics, and web- based automation, this project provides an intelligent, proactive, and scalable solution for monitoring and managing crowd density, ensuring safer and more efficient public spaces.