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.
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Updated
Mar 23, 2025 - Python
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.
Crowd detection system powered by YOLOv8 and OpenCV. Features modular architecture, video processing pipeline, and detailed performance analysis for urban environments.
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).
Person detection and density analysis using YOLOv8. Evolution of CrowdCluster.
Crowd detection system for YIC 2023
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.
Final Project for [CS-1390] IML 👀
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.
Offline CrowdAware system for Raspberry Pi 4B and Heltec LoRa V3 using Raspberry Pi Camera Module 3 and MLX90640 Thermal Camera.
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