Build an AI/ML Tennis Analysis system with YOLO, PyTorch, and Key Point Extraction
Education
Introduction
In this article, we will walk through building a comprehensive tennis analysis system that leverages AI and Machine Learning technologies—specifically, YOLO (You Only Look Once) for object detection, PyTorch for model training, and key point extraction for player and ball dynamics analysis. This system aims to detect and track players and tennis balls throughout a video, measure their speeds, determine distances covered, and assess shot accuracy.
Overview of the Project
The first step in our project involves setting up a directory containing input videos. This folder will house a single image screenshot of a tennis match and a video clip from the same match. We'll utilize these resources for developing and testing our code.
Step 1: Player and Ball Detection
We will utilize a popular object detection model, YOLO, to detect players and tennis balls in our video. The Ultralytics library provides a simple way to implement YOLO. Start by installing the library using:
pip install ultralytics
Next, we will create a Python script—‘YOLO inference’—where we will import the YOLO model and examine how it functions.
from ultralytics import YOLO
## Introduction
model = YOLO('yolov8n.pt')
## Introduction
results = model.predict('input_videos/image.png', save=True)
Once we run this script, we’ll observe an output folder named "runs" with a subfolder containing the predicted outputs overlaid on the original image. Each detected object will display its bounding box, classification label, and confidence score.
Step 2: Detecting the Tennis Ball with Enhanced Accuracy
The YOLO model will detect players effectively, but tennis balls may require fine-tuning since they're fast-moving objects. We can create a new training dataset for tennis balls using RoboFlow, a tool that can facilitate easy annotation and model training.
We'll set up our training environment in a Jupyter Notebook, train the YOLO model specifically for tennis balls, and integrate it into our detection code.
Step 3: Player Speed and Distance Measurement
In our project, we will also measure player speed and the distance they cover during the game. This will entail tracking player movements across frames using the ID generated by YOLO for consistent reference.
We'll set up a tracker in another Python script and ensure that we can obtain the player's coordinates to compute covered distances and speeds efficiently.
Step 4: Key Point Detection
Using techniques from key point extraction models, we can detect critical points on the court that help us assess player location relative to specific areas, such as service lines. This will assist in determining if the ball was in or out during gameplay.
Step 5: Generating Player Stats and Video Output
Finally, we will compile all detected information into comprehensive statistics that provide insights into player performance. After processing, we will produce an output video that displays each player’s speed, shot counts, and speed metrics visually on the mini court.
## Introduction
def draw_player_stats(output_video_frames, player_stats):
for frame in output_video_frames:
# Overlaying stats on frames
# Logic to draw stats...
Conclusion
Following these steps, you will have a functioning tennis analysis system capable of implementing AI and Machine Learning methodologies effectively. Not only will this project showcase technical competency, but it also enhances your portfolio.
Keyword
- AI
- ML
- Tennis analysis
- YOLO
- PyTorch
- Object detection
- Key point extraction
- Player detection
- Ball tracking
- Speed measurement
- Distance covered
FAQ
Q1: What is the primary purpose of the AI/ML tennis analysis system?
A1: It aims to detect and track players and tennis balls within a video, measure player speeds, calculate distances covered, and assess shot accuracy.
Q2: Which technology is used for object detection in this project?
A2: The project utilizes the YOLO (You Only Look Once) model for object detection.
Q3: How do we improve the accuracy of detecting fast-moving objects like tennis balls?
A3: By fine-tuning a YOLO model specifically trained on tennis ball datasets created using RoboFlow.
Q4: What metrics does the tennis analysis system provide?
A4: The system provides metrics including shot speeds, player speeds, distance covered, and overall performance statistics.
Q5: Can this project be expanded for different sports?
A5: Yes, the underlying principles and methodologies can be adapted for various sports analysis by modifying the dataset and detection logic accordingly.
