In this video, you'll learn how to use machine learning, computer vision and deep learning to create a tennis analysis system. This project utilizes YOlO a state of the art object detector to detect the players and the tennis balls. It also utilizes trackers to track those object across frames. We also write our own conveloutional Nueral network to detect court key points. Github link is provided bellow.

In this video you will learn how to:
1. Use ultralytics and YOLOv8 to detect objects in images and videos.
2. Fine tune and train your own YOLO on your own custom dataset.
3. Train a CNN with pytorch to extract keypoints.
4. Use object trackers to track objects across frames.
5. Use CV2 to read, manipulate and save a video.
6. Analyze detection data and take a data driven approach to develop features.
7. Put all those ML/DL model output into one big project that have a concrete output.

Robowflow Tennis ball Dataset: [ Ссылка ]

Github Link: [ Ссылка ]

🔑 TIMESTAMPS
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0:00 - Introduction
1:00- Object detection with YOLO
11:30 - Train YOLO on tennis balls
22:35- Object Tracking
25:40- Train key point detection with Pytorch
50:55- Tennis Analyzer

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