Welcome to my AI Tennis Analyzer, an advanced machine learning and deep learning project designed to revolutionize how we understand and analyze tennis. Leveraging state-of-the-art technologies, this project offers comprehensive insights into tennis matches by detecting objects in images and videos, tracking objects across frames, analyzing detection data, and employing data-driven methodologies for enhanced feature development.
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Object Detection with YOLOv8: Utilized the cutting-edge Ultralytics YOLOv8 model for high-precision object detection within tennis matches, enabling the identification and classification of key elements in real-time.
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Custom Model Training on A100 GPUs: Fine-tuned YOLOv5 on a custom dataset to improve tennis ball tracking accuracy. Training was performed on NVIDIA A100 GPUs through Google Colab and Jupyter Notebooks, ensuring rapid processing and superior model performance.
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Keypoint Extraction with CNNs: Employed Convolutional Neural Networks (CNNs) within PyTorch to accurately extract keypoints from the tennis court, facilitating detailed motion analysis and player positioning strategies.
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Advanced Object Tracking: Integrated sophisticated object tracking algorithms to maintain continuity across frames, providing seamless analysis of player movements and ball trajectories throughout rallies.
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Video Processing with OpenCV: Leveraged OpenCV (cv2) for comprehensive video processing capabilities, including reading, manipulating, and saving video data for further analysis.
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Data Analysis with Pandas: Utilized the Pandas library for efficient data manipulation and analysis, enabling sophisticated handling of datasets to derive meaningful insights and inform feature development.
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Data-Driven Feature Development: Analyzed detection data to iteratively develop and refine project features, adopting a data-driven approach to enhance the accuracy and utility of the analytics provided.
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Comprehensive Machine Learning Pipeline: Combined multiple ML/DL models into a unified project with tangible outputs, showcasing the potential of AI in transforming tennis analysis.
- YOLOv8 & Ultralytics: For cutting-edge object detection and model fine-tuning.
- PyTorch: The backbone for CNN model development and training.
- Google Colab & Jupyter Notebooks: Flexible and powerful platforms for developing and executing deep learning models.
- OpenCV (cv2): For all video reading, processing, and saving needs.
- Pandas: For data analysis and manipulation, enabling effective feature development and insights extraction.
- Interpolation Object Tracking Algorithms: To track the movement of players and the ball across video frames.