This project implements real-time crowd detection using YOLOv8. It identifies and tracks people in a video feed, detects crowd formation based on proximity, and logs the results in a CSV file.

Real-time crowd detection using TensorFlow.js and machine learning

Computer vision system that detects and spatially clusters groups of people in static images using a custom-trained Haar Cascade classifier and a Winner-Takes-All network implemented from scratch in MATLAB. Academic project from my Neuro-Fuzzy Systems course at UPIITA–IPN (2023).

Intelligent crowd monitoring system combining YOLO-based people detection with a MERN architecture to analyze crowd density and provide real-time monitoring through an interactive dashboard.

Person detection and density analysis using YOLOv8. Evolution of CrowdCluster.

Ezycrowd is an AI-driven smart crowd detection and management system leveraging computer vision to analyze real-time crowd density and generate alerts for improved public safety.

An advanced platform that combines satellite computer vision, mobile network analytics, and machine learning to provide real-time and predictive crowd density analysis for tourism destinations.

Crowd detection system for YIC 2023

A real-time computer vision system for monitoring and analyzing crowd density using YOLO-based object detection and tracking.

A real-time crowd detection system built with TypeScript that analyzes webcam/video input to detect and count people, estimating crowd density for smart surveillance and safety monitoring

Final Project for [CS-1390] IML 👀

Crowd detection system powered by YOLOv8 and OpenCV. Features modular architecture, video processing pipeline, and detailed performance analysis for urban environments.

라즈베리파이 + YOLOv8으로 실내 혼잡도를 감지하고 웹 대시보드로 실시간 시각화하는 AIoT 시스템

Offline CrowdAware system for Raspberry Pi 4B and Heltec LoRa V3 using Raspberry Pi Camera Module 3 and MLX90640 Thermal Camera.