Tennis Player & Ball Detection and Tracking System, YOLOv8, and DeepSORT. Field Keypoints detection and homography.
Sub Category
- Data Science
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Objectives
- Build a complete, end-to-end sports analytics system using Python.
- Train and implement a YOLOv8 model for high-speed, real-time ball detection
- Use Grounding DINO to detect players with text prompts (zero-shot detection).
- Implement DeepSORT to track multiple players and maintain their unique identities.
- Master homography with OpenCV to transform a camera view into a 2D top-down map.
- Train a custom YOLOv8-Pose model to accurately detect key points on a court.
- Visualize player and ball movements on a 2D tactical map for strategic analysis.
- Combine multiple AI models into a single, cohesive data pipeline.
- Develop a portfolio-worthy project in the exciting field of AI in sports.
- Understand the core principles of object detection, tracking, and perspective transformation.
- Process and analyze complex video data to extract meaningful insights.
- Prepare custom datasets for training advanced computer vision models.
Pre Requisites
- Basic Python Programming Skills
- Fundamental Understanding of Machine Learning
- Basic Deep Learning Concepts
- Experience with Jupyter Notebooks or Google Colab
- Familiarity with data science and computer vision libraries
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)