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📌 Project Overview

Color-Based Ball Tracking With OpenCV is a computer vision project that uses color segmentation to track the movement of balls in a video. It detects the color of each ball, determines its position in frame quadrants, logs entry and exit times, and generates an annotated output video showing tracking details.

🎥 Demo & Files

  • Input Video: AI Assignment video.mp4
  • Output Video: processed_video.avi
  • Event Log: events.csv

The output video contains overlays indicating the ball's position, direction, and quadrant transitions. All events are timestamped and saved for review in the CSV file.

🗂️ Project Structure


📁 Color-Based-Ball-Tracking-With-OpenCV/
├── .gitattributes                  # Git settings
├── AI Assignment video.mp4        # Input video file for tracking
├── LICENSE                        # License file (MIT)
├── README.md                      # Project documentation
├── events.csv                     # Output log of events (entry/exit times)
├── processed\_video.avi            # Annotated output video
└── trani model.ipynb              # Main Jupyter notebook (typo: 'trani' → 'train' or 'tracking')

⚙️ Installation

1. Clone the repository

git clone https://github.com/your-username/Color-Based-Ball-Tracking-With-OpenCV.git
cd Color-Based-Ball-Tracking-With-OpenCV

2. Install dependencies

Make sure you have Python 3.7+ and Jupyter installed. Then run:

pip install opencv-python numpy imutils

Or install from within the notebook.

🚀 Getting Started

  1. Open the Jupyter notebook:
jupyter notebook "trani model.ipynb"

  1. Run the cells in sequence:

    • Load and preprocess the video
    • Apply HSV-based color masking
    • Detect ball contours and calculate positions
    • Log entry/exit events based on quadrant transitions
    • Write results to processed_video.avi and events.csv

  2. Review the output files in the root directory.

📊 Sample Event Log (events.csv)

Timestamp Color Event Quadrant
00:00:05.12 Red Entered Q1
00:00:06.45 Red Exited Q2
00:00:07.21 Green Entered Q3

🧪 Features

  • 🎯 Color Detection using HSV thresholding
  • 🧭 Quadrant Detection in a 2x2 frame split
  • 📝 Event Logging (entry & exit per quadrant)
  • 🎥 Processed Video Output with overlays

🛠️ Customization

You can easily customize the project:

  • 🎨 Change tracked colors by modifying HSV ranges
  • 📐 Adjust quadrant logic to fit other layouts
  • 🖼️ Change video resolution or format
  • 📄 Log additional metrics like speed or trajectory

📚 Use Cases

  • 🏀 Sports video analysis
  • 🤖 Robotics vision systems
  • 🎓 Educational demos for OpenCV and tracking
  • 📹 Surveillance and area monitoring

🤝 Contributing

Feel free to fork this repo and submit pull requests. For any major changes, please open an issue first.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

✏️ Notes

  • The file name trani model.ipynb contains a typo. Consider renaming it to tracking_model.ipynb for clarity.
  • You can convert this notebook to a Python script using:
jupyter nbconvert --to script "trani model.ipynb"