The document describes a proposed system to monitor crowds in public places using neural networks and computer vision. The system would use a camera to capture video feeds of areas like temples or company events. An object detection model trained on neural networks would detect and track humans in the video. It would count the number of people and control entry gates as needed to avoid overcrowding. The proposed system architecture includes components for video capture, object detection/tracking using a neural network model, data storage, application control interface, and GUI display. It then outlines the object detection and tracking process which involves detecting new objects, associating IDs to tracked objects, and deregistering lost objects. The output shows sample terminal outputs of the system initializing, tracking people