AI-Based Crowd Density Analysis for Railway Stations

The project titled “AI-Based Crowd Density Analysis for Railway Stations” focuses on developing an intelligent and automated system to monitor and analyze crowd density in real time. In busy railway stations, managing large crowds is a major challenge that can lead to safety risks, congestion, and inefficient resource management. To address this issue, the proposed system integrates Artificial Intelligence and embedded systems for smart surveillance. In this system, a webcam is used to capture live video footage of the railway station environment. The captured images are processed using the YOLO (You Only Look Once) algorithm in Python, which is highly efficient for real-time object detection. The model detects and counts the number of people in a given frame to estimate crowd density accurately. An Arduino microcontroller is used to support sensor interfacing and system control operations. When the crowd density exceeds a predefined threshold, the system automatically triggers an alert mechanism. It sends an email notification along with the detected location information, enabling authorities to take immediate action. This real-time monitoring and alert system helps improve passenger safety, reduce overcrowding, and optimize crowd management in railway stations.