System integrated with YOLOv4 and Deep SORT for real-time crowd monitoring, then perform crowd analysis. The system is able to monitor for abnormal crowd activity, social distance violation and restricted entry. The other part of the system can then process crowd movement data into optical flow, heatmap and energy graph.

Face Recognition from Crowd by using yolov7 .Extracting the faces from a video/image/live source, which is then passed to the custom facenet network in order to recognize the peoples

A machine learning-based social distance estimation and crowd monitoring system for surveillance cameras

Crowd monitoring and management using real-time data from IP camera and Laptop camera footage which aims to provide users with insights into the crowd density at various locations espicially at local market places , shops ,malls. This helps users make informed decisions about visiting places based on the level of crowdiness.

Full-stack web application for the visualization of road attributes and route planning in Amsterdam using area avoidance, focusing on the needs of the elderly and vulnerable people

AI-powered multi-camera surveillance with posture classification, fall/inactivity detection, heatmaps, and real-time alerts. Built using OpenCV, MediaPipe, Streamlit & Flask.

Corwd Monitoring Fullstack IOT enabled app.

The Doorway Traffic Counter project is a real-time monitoring system designed to track and analyze foot traffic through an entryway. It provides essential features like traffic counting, data analysis, and customizable notifications, with potential for future enhancements to improve user experience and functionality.

it focuses on real-time emotion analysis in crowds using Convolutional Neural Networks (CNN) and OpenCV. Developed in Python, the system detects and analyzes facial emotions to provide valuable insights for event management and public safety.