Yolo-Person-Detection

Source code on GitHub.

README

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

Clone this repository.

Set up a virtual environment and install dependencies:

```bash # 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