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A comprehensive computer vision platform for counting people entering and exiting through doorways, gates, or other defined boundaries. Features YOLO11 object detection, BoT-SORT tracking, FastAPI backend, and an intuitive Streamlit web interface.

Footfall Counter using Computer Vision

An AI-based Footfall Counter using YOLOv5 and OpenCV for detecting and counting people in video feeds.

To develop a computer vision–based system that counts the number of people entering and exiting through a specific area (like a doorway, corridor, or gate) using object detection, tracking, and region-of-interest (ROI) logic.

Footfall counter using computer vision that detects, tracks, and counts people entering and exiting through a gate using YOLO and OpenCV.

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A Python-based computer vision project that detects and tracks people in a video stream to count entries and exits across a defined region of interest (ROI) using YOLOv8 and DeepSORT. Built with OpenCV and Ultralytics, this project demonstrates object detection, tracking, and counting logic for footfall analytics.

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Develop a computer vision-based system that counts the number of people entering and exiting through a specific area (such as a doorway, corridor, or gate) in a video. The goal is to demonstrate your understanding of AI model integration, tracking logic, and end-to-end problem-solving.

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AI Assignment – Footfall Counter using Computer Vision

A footfall counting system using two methods: OpenCV-based background subtraction and YOLOv8 deep learning detection. People are tracked using centroid tracking, and IN/OUT counts are computed when they cross a defined ROI line. The project compares accuracy, speed, and real-time performance.

AI-powered footfall counter using computer vision to detect, track, and count people crossing virtual boundaries in real-time ```

AI-powered Footfall Counter using Computer Vision to detect, track, and count people entering or exiting a defined region in real-time. Built with Python, OpenCV, and YOLO for accurate detection and tracking. Ideal for crowd analysis, smart surveillance, and store analytics.

To design an AI-based Footfall Counter System that automatically detects and counts the number of people entering and exiting a specific area using Computer Vision techniques. This project demonstrates real-world applications of AI in retail analytics, crowd monitoring, and smart surveillance systems.