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Object_Tracking-Web-

A real-time object detection and tracking web application using YOLOv8, OpenCV, and Flask. The app streams video from a webcam or file, performs object tracking using YOLOv8, and displays the output via a Bootstrap-styled frontend.

YOLOv8 Object Tracking Web Application

A web-based application for real-time object tracking using YOLOv8 and FastAPI. This project provides a user-friendly interface for uploading and processing videos with state-of-the-art object detection and tracking capabilities.

Features

• Real-time Object Detection & Tracking: Utilizes YOLOv8 for accurate object detection and implements tracking algorithms to follow objects across video frames
• Interactive Web Interface: User-friendly dashboard for uploading, processing, and analyzing videos
• Detailed Metrics: Real-time display of FPS, object counts, confidence scores, and processing times
• Visual Tracking: Displays bounding boxes, object trails, and unique IDs for each tracked object
• Video Processing: Upload your own videos or use local video files for processing
• Save Processed Videos: Option to save the processed videos with tracking visualization

Technologies Used

• Backend: FastAPI, Python
• Object Detection: YOLOv8 (Ultralytics)
• Frontend: HTML, CSS, JavaScript, Bootstrap 5
• Video Processing: OpenCV
• Data Visualization: Custom JavaScript for real-time metrics display

Installation

1. Clone the repository:

   git clone https://github.com/ManeeshProg/Object_Tracking-Web-.git
   cd Object_Tracking-Web-

2. Install the required dependencies:

   pip install -r requirements.txt

3. Download the YOLOv8 model (if not already included):

   # The model will be downloaded automatically when first running the application
   # Or you can manually place yolov8n.pt in the project root directory

Usage

1. Start the FastAPI server:

   uvicorn main:app --host 0.0.0.0 --port 8000

2. Open your web browser and navigate to:

   http://localhost:8000
   

3. Use the web interface to:

   • Upload a video file for processing
   • Select a local video file from your system
   • View real-time object tracking with metrics
   • Save the processed video with tracking visualization

Project Structure

Object_Tracking-Web-/
├── main.py                # FastAPI application and backend logic
├── index.html             # Main web interface
├── static/                # Static files (CSS, images)
│   └── styles.css         # Custom CSS styles
├── outputs/               # Directory for processed video outputs
├── yolov8n.pt             # YOLOv8 nano model weights
└── requirements.txt       # Python dependencies

Customization

• Model Selection: You can replace yolov8n.pt with other YOLOv8 models (s, m, l, x) for different accuracy/speed trade-offs
• Tracking Parameters: Adjust tracking parameters in the code to optimize for your specific use case
• UI Customization: Modify the HTML and CSS files to customize the user interface

Requirements

• Python 3.8+
• FastAPI
• Ultralytics YOLOv8
• OpenCV
• NumPy
• Modern web browser with JavaScript enabled

License

This project is open-source and available under the MIT License.

Acknowledgments

• Ultralytics YOLOv8 for the object detection model
• FastAPI for the web framework
• OpenCV for video processing capabilities
• Bootstrap for frontend components

680be56 (Intern First_commit)