# Vehicle Tracking with Machine Learning and Computer Vision Welcome to the **Vehicle Tracking with Machine Learning and Computer Vision** project! This open-source project aims to provide an easy-to-use solution for tracking vehicles in videos. Whether you're analyzing traffic patterns, conducting research, or just curious about what's on the road, this project has you covered. ## Features - **Upload Any Video:** Users can upload videos of their choice for vehicle tracking. - **Vehicle Tracking:** Our state-of-the-art machine learning and computer vision algorithms will track vehicles in the video. - **Output Video:** The program generates an output video with tracked vehicles highlighted, providing a visual representation of the tracking results. - **Configurable Options:** Fine-tune tracking parameters to match your specific use case. ## How it Works 1. **Upload Video:** Users upload a video to the application. 2. **Tracking:** The program processes the video frame by frame, detecting and tracking vehicles using advanced computer vision techniques. 3. **Output Video:** A new video is generated with tracked vehicles highlighted for easy visualization. ## Getting Started To get started with vehicle tracking, follow these simple steps: 1. Clone this repository: `git clone https://github.com/Edward9292/vehicle-tracking-project.git` 2. Install the required dependencies using `pip install -r requirements.txt`. 3. Run the application and upload your video. 4. Explore the tracked vehicle results in the generated video. ## Dependencies - Python 3.x - OpenCV - TensorFlow (or other ML framework) - [Additional dependencies listed in requirements.txt] ## Contributing We welcome contributions from the community! If you have ideas for improvements, bug fixes, or new features, please open an issue or submit a pull request. Check out our [Contributing Guidelines](CONTRIBUTING.md) for more details. ## License This project is licensed under the [MIT License](LICENSE). ## Acknowledgments We would like to express our gratitude to the open-source community and the creators of the libraries and tools that make this project possible.

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