🚗 Vehicle Tracking 🛣️

This project leverages a fine-tuned YOLO model for vehicle detection and tracking, enabling automated counting of various vehicle types as they cross a designated line using a single camera or video feed. By replacing manual tracking methods with advanced computer vision techniques, the system provides accurate real-time data on traffic flow, which can be used to inform transportation control decisions.

✨ Features

• 🚙 Vehicle Detection: Detects multiple types of vehicles (e.g., cars, trucks, motorcycles).
• 🔢 Vehicle Counting: Counts vehicles as they cross a specific line in the frame.
• 📊 Classification: Classifies vehicles into predefined categories.

📁 Project Structure

• data/: Dataset configurations and preprocessing scripts
• models/: YOLO model configuration and weights
• notebooks/: Jupyter notebooks for model training and testing
• streamlit_app/: App with streamlit
• README.md: Project overview and usage instructions

⚙️ Setup

1. Clone the repository:

   git clone https://github.com/angelTCC/VehicleTracking.git

2. Install Dependencies: bash pip install -r requirements.txt

   cd VehicleTracking
   python3.10 -m venv .env
   pip install -r requeriments.txt

3. Download YOLO Weights: Download pretrained weights and place them in the models/ directory.

4. Run the application:

   cd streamlit_app
   streamlit run Home.py

📈 Results

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

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📜 License

This project is licensed under the MIT License.

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