Real-time object detection using YOLO model and OpenCV on live camera feed.
The "Live Object Detection with YOLO and OpenCV" project is a real-time object detection system that utilizes the YOLO (You Only Look Once) model and the OpenCV library to perform live object detection on a camera feed. This project aims to showcase the potential of real-time object detection and inspire developers, researchers, and hobbyists to explore the exciting world of computer vision applications.
Main Objectives:
- Real-Time Object Detection: Detect and identify various objects in a live video stream in real-time.
- YOLO Model Integration: Utilize the speed and accuracy of the YOLO model for efficient object detection.
- OpenCV Visualization: Visualize the detected objects with bounding box annotations using OpenCV.
- Efficiency: The YOLO model enables fast and reliable object detection in real-time, suitable for various applications.
- Customization: Users can adjust the confidence threshold, modify annotation styles, and select specific object classes for detection.
- Ease of Use: The code is user-friendly, with comprehensive documentation to guide users in setup and customization.
- Versatility: The live object detection system can be used for security, surveillance, traffic monitoring, and interactive installations.
To get started with the project, follow the installation and setup instructions in the Installation Guide. This will help you set up the necessary environment and install required dependencies.
- Make sure you have followed the installation instructions.
- Run the Jupyter notebook "demo.ipynb" to start the live object detection demo.
- Your webcam or connected camera will display a live video stream with object detection annotations.
- Press the 'q' key to stop the program and close the video window.
The project allows you to customize various aspects of the live object detection system:
- Adjust the confidence threshold for object detection.
- Modify the appearance of bounding box annotations.
- Select specific object classes to detect or exclude.
Refer to the Customization Guide for detailed instructions on how to customize the project.
Explore the Examples section to discover practical use cases and scenarios for the live object detection system.
If you encounter any issues during setup or execution, refer to the Troubleshooting Guide for common solutions to potential problems.
Contributions to the project are welcome! If you find a bug, have a feature request, or want to contribute improvements, please check out our Contributing Guidelines.
I would like to acknowledge the contributions of the open-source community, the creators of YOLO, and the developers of OpenCV.
This project is licensed under the MIT License.
Thank You -Tinny Robot