-Detect-and-classify-different-types-of-vehicles-car-bus-truck-using-basic-feature-extraction.

This Python project detects and classifies vehicles (bike, car, truck) from a video feed using OpenCV. It applies a bird’s eye view transformation to better track and analyze vehicle motion within a defined Region of Interest (ROI).

🚗 Vehicle Detection with Bird’s Eye View Perspective

📸 Features

• Manual ROI selection via 4-point mouse click
• Perspective transformation to top-down view
• Background subtraction using KNN (with shadow detection disabled)
• Vehicle detection using contour analysis
• Vehicle classification into:
  • Bike
  • Car
  • Truck

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🧰 Requirements

• Python 3.x
• OpenCV (cv2)
• NumPy

📦 Install dependencies:

pip install opencv-python numpy

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🖱️ Select ROI

Click 4 points on the first video frame in the order:

1. Top-left
2. Top-right
3. Bottom-right
4. Bottom-left

These define the area for the perspective transformation.

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🎥 View Detection

• A top-down bird’s eye view window with real-time vehicle detection
• Original video with the selected ROI highlighted
• Foreground mask showing motion-based areas
• Press Esc to exit the program

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🧠 Vehicle Classification Logic

Vehicles are classified using bounding box area and aspect ratio:

if area > 50000:
    return "truck"
elif area > 7000:
    return "Car"
else:
    return "bike"

Adjust these thresholds in the classify_vehicle() function for better results depending on your video input.

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🔮 Future Improvements

• Add vehicle tracking with unique IDs (e.g., using SORT or Deep SORT)
• Improve classification using a trained machine learning or deep learning model
• Count vehicles crossing a virtual line or zone
• Export analytics (e.g., vehicle count per type, estimated speed, timestamps, etc.)

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📄 License

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