Step 1: Set up your development environment (e.g., Python with OpenCV). Step 2: Write initial code to load the provided videos and process each frame. Step 3: Implement the detection algorithm to identify the ball. Step 4: Develop the tracking and enhancement mechanisms. Step 5: Test with provided video snippets and adjust for challenging conditions like low lighting or occlusions. Step 6: Record a demonstration video showcasing your algorithm in action. Step 7: Prepare a detailed report, including algorithm explanations, challenges faced, and evaluations.
object_tracking_project/ │ ├── data/ │ ├── videos/ │ │ ├── input_video1.mp4 │ │ ├── input_video2.mp4 │ │ └── ... (other provided video files) │ └── output/ │ ├── tracked_video1.mp4 │ ├── tracked_video2.mp4 │ └── ... (output videos after processing) │ ├── src/ │ ├── main.py │ ├── ball_detection.py │ ├── ball_tracking.py │ ├── enhancement.py │ └── utils.py │ ├── notebooks/ │ ├── exploration.ipynb │ └── testing.ipynb │ ├── models/ # If you decide to use pre-trained models or save trained models │ └── ... (saved model files, if any) │ │ ├── requirements.txt # Python dependencies │ ├── README.md # Project overview and instructions │ ├── report/ │ ├── report.docx # Detailed report with sections │ └── presentation.mp4 # Pre-recorded project presentation │ └── config/ ├── settings.yaml # Configuration for different parameters └── logging.conf # Logging configuration