YOLO Python Scripts Collection

This repository contains multiple Python scripts utilizing the YOLO model for various real-time object detection and pose estimation tasks, including fall detection and general object recognition.

Overview

The scripts here use the YOLO model from the Ultralytics library for detecting objects and poses in video feeds, primarily via webcam. YOLO (You Only Look Once) is an advanced, high-speed object detection model, making it suitable for real-time applications.

Features

• Pose Detection: Detects human poses and highlights keypoints to monitor body orientation, with fall detection alerts when unusual body posture is identified.
• Facial Detection: Recognizes facial features and keypoints to monitor entry way or apply filters.
• Object Recognition: Identifies various object classes (e.g., person, vehicle, animals) in real-time video.
• Customizable Thresholds: Adjusts confidence thresholds for object detection and classification.

Requirements

• Python
• OpenCV
• Ultralytics YOLO library

Usage

1. Clone this repository.

2. Set up a virtual environment and install dependencies:

   
   # Step 1: Navigate to the project directory
   cd path/to/your/project
   
   # Step 2: Create a virtual environment named 'venv'
   py -m venv venv
   
   # Step 3: Activate the virtual environment
   # On macOS and Linux
   # source venv/bin/activate
   # On Windows
   venvScriptsactivate
   
   # Step 4: Install dependencies from requirements.txt
   pip install -r requirements.txt
   
   # Step 5 (Optional): Verify installed packages
   pip list
   
   # Step 6: Deactivate the virtual environment when done
   deactivate

Imported from gh:Kentucky-Open-Science/Yolo-Person-Detection. Source last updated 2025-12-18. Synced 2026-07-27.