Object Detection in Traffic Video using OpenCV and Python Overview This project demonstrates the implementation of object detection using OpenCV and Python, specifically focusing on detecting cars in a traffic video. The goal is to identify and track vehicles within a given video stream, providing a practical application for real-world scenarios such as traffic monitoring and management.
Features Object Detection: Utilizing the OpenCV library, the project implements a robust object detection algorithm to identify and locate cars in each frame of the video.
Tracking: The application incorporates object tracking to follow the detected cars across consecutive frames. This helps in understanding the movement and behavior of vehicles over time.
Video Input: The system is designed to work with standard video formats, making it easy to integrate with various traffic surveillance systems or pre-recorded video footage.
Visualization: The project includes visualization tools to draw bounding boxes around detected cars, providing a clear representation of the object detection process.
Dependencies Make sure you have the following dependencies installed before running the project:
Python 3.x OpenCV NumPy
Install the required packages using the following command: pip install opencv-python numpy
Feel free to customize the project for your specific use case. You can adjust parameters in the code, such as confidence thresholds, object tracking algorithms, or even integrate with other computer vision techniques.
Contributing If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. Contributions are welcome!
License This project is licensed under the MIT License - see the LICENSE.md file for details.
Acknowledgments The project was inspired by the need for efficient traffic monitoring solutions. Thanks to the OpenCV community for providing a powerful computer vision library. Feel free to modify the content to better suit the specifics of your project and include any additional details that might be relevant.
Note: 1.the YOLOV4 config files are present in the dnn_model folder can be used directly 2.prefer doing this on pycharm instead of jupyter notebook