Football AI Tutorial: From Basics to Advanced Stats with Python
Updated: November 19, 2024
Summary
The video showcases the intricate process of utilizing advanced AI techniques like homography, keypoint detection, and object tracking to analyze football games. It covers the training of custom object detection models and implementing perspective transformations for accurate player tracking and ball trajectory analysis. The project emphasizes the significance of accuracy in keypoint detection and model optimization for real-time applications in sports analytics, offering insights into future advancements in football AI such as predictive modeling and enhanced player metrics.
TABLE OF CONTENTS
Introduction to Football AI Project
Diagram of Models and Tools
Perspective Transformation and Tracking
Player Division and Radar View
Object Detection and Model Training
Training Object Detector Model
Data Set Preparation and Training
Benchmarking Model and Uploading to Universe
Coding in Google Colab
Object Detection and Model Analysis
Video Frame Processing and Annotations
Tracking Players and Goalkeepers
Player Division into Teams
Introduction to Homography
Calculating Homography Matrix
Homography for Perspective Transformation
Challenges in Using Homography
Training YOLO V8 Keypoint Detection Model
Labeling and Post-Processing
Model Training Process
Training Data Set Download
Model Training and Optimization
Model Performance Evaluation
Model Deployment and Inference
Homography Transformation Implementation
Transforming Key Points
Visualizing Transformed Points
Creating Horizontal Flip Augmentations
Building Projected Lines
Model Results Analysis
Deploying Model for Inference
Creating Text Cell and Loading Model
Performing Key Point Detection
Visualizing Anchors and Skeleton
Enhancing Key Point Detection Results
Preparing for Homography Transformation
Performing Perspective Transformation
Visualizing Point Transformation
Transforming Camera View to Peach View
Visualizing Transformed Players and Points
Wrapping Up Point Transformation
Cleaning up Results and Trajectory Analysis
Stability in Ball Tracking
Data Cleaning for Multiple Ball Detections
Visualizing Ball Trajectory
Trajectory Data Cleaning
Considerations for Football AI
Challenges in Perspective Transformation
Key Point Detection Accuracy
Optimizing Model Efficiency
Future Exploration in Sports Analytics
Introduction to Football AI Project
The speaker introduces the project, discussing the use of Sly embeddings to divide players into teams, key detection, and homography to create a radar view, and the use of stats like ball trajectory and Voronoi diagram to illustrate team control over the pitch.
Diagram of Models and Tools
A diagram illustrating the models and tools used in the project, including object detection and keypoint detection models fine-tuned on custom datasets for players, goalkeepers, referees, and the ball.
Perspective Transformation and Tracking
A detailed explanation of using perspective transformation to detect object locations on the pitch and track their movement using unique Tracker IDs assigned by B-track algorithm.
Player Division and Radar View
Using embedding analysis, U-map for dimensionality reduction, and K-means clustering to divide players into teams and create a radar view showing player positions and ball movement.
Object Detection and Model Training
Explains the use of YOLO V8 object detector trained on a custom dataset for detecting ball, goalkeepers, players, and referees for analyzing football games.
Training Object Detector Model
Detailed steps on training object detection models using Google Colab with GPU for free, setting up API keys, installing Python dependencies, and downloading the dataset.
Data Set Preparation and Training
Discusses the preparation of the dataset, including post-processing images, handling ball detection challenges, adjusting input resolution for better ball detection, and training the model on Google Colab with GPU acceleration.
Benchmarking Model and Uploading to Universe
Explains benchmarking the model using mean average precision, uploading the fine-tuned model to Roboflow Universe, and preparing the environment for coding in Google Colab for further analysis.
Coding in Google Colab
Walkthrough of setting up the coding environment in Google Colab, installing necessary Python dependencies, downloading source videos, and utilizing utilities for visualization and analysis.
Object Detection and Model Analysis
Detailed explanation of running object detection models on frames, visualizing detections using annotators, and improving visualizations with advanced annotators like ellipse and triangle annotators.
Video Frame Processing and Annotations
Demonstrates how to process video frames, run the model on frames, display bounding boxes, labels, and confidence levels, and optimize visualizations for better object detection results.
Tracking Players and Goalkeepers
Explains the tracking of players, goalkeepers, and referees using annotations, updating tracker IDs, and visualizing the tracking results with annotations and team classifications.
Player Division into Teams
Details the process of dividing players into two teams using embeddings, dimensionality reduction with U-map, and clustering with K-means to accurately classify players into teams.
Introduction to Homography
Explanation of homography and its importance in tracking player positions and ball movements in football AI.
Calculating Homography Matrix
Detailed explanation of how the homography matrix is calculated using corresponding sets of points in images for perspective transformation.
Homography for Perspective Transformation
Using homography for perspective transformation, especially in situations where the camera is static.
Challenges in Using Homography
Discussion on the challenges faced when using homography in dynamic camera settings at sports events.
Training YOLO V8 Keypoint Detection Model
Explanation of training a YOLO V8 keypoint detection model to identify characteristic points on a football field.
Labeling and Post-Processing
Description of labeling characteristic points on the football pitch and post-processing steps after labeling.
Model Training Process
Overview of training the keypoint detection model and utilizing Google Colab for training.
Training Data Set Download
Steps to download the data set for training the model from the Roboflow Universe and setting up the Google Colab environment.
Model Training and Optimization
Discussion on model training, optimization settings, and the importance of adjusting parameters like batch size and augmentation techniques.
Model Performance Evaluation
Evaluating the model performance on validation data and the impact of training duration on prediction quality.
Model Deployment and Inference
Deploying the trained model on Roboflow Universe and using it for inference locally and through the Roboflow API.
Homography Transformation Implementation
Implementing homography transformation using source and target points for perspective change in football AI.
Transforming Key Points
Transforming key points detected in the camera view to the football pitch view using the implemented perspective transformation.
Visualizing Transformed Points
Visualizing the transformed points on the football pitch and the significance of homography in maintaining accurate positions.
Creating Horizontal Flip Augmentations
Explanation of the flip index section in the dataset for creating horizontal flip augmentations and enhancing model performance.
Building Projected Lines
Building projected lines using keypoint detection results and homography transformation for accurate visualization in football AI.
Model Results Analysis
Analyzing model results, addressing minor errors in keypoint detection, and optimizing the model for stability and accuracy.
Deploying Model for Inference
Deploying the model on Roboflow Universe and using it for inference in a football AI project.
Creating Text Cell and Loading Model
Creating a text cell, loading the model from Roboflow Universe, and importing necessary functions for football AI implementation.
Performing Key Point Detection
Performing key point detection on frames using the loaded model and visualizing key points on the screen.
Visualizing Anchors and Skeleton
Utilizing vertex annotator to visualize anchors of the skeleton detected in keypoint detection for player tracking in football AI.
Enhancing Key Point Detection Results
Filtering out key points with low confidence levels and optimizing key points for accurate detection in football AI.
Preparing for Homography Transformation
Preparing key points for homography transformation by filtering out redundant points and setting up reference key points.
Performing Perspective Transformation
Performing perspective transformation using source and target points to map key points accurately in football AI.
Visualizing Point Transformation
Visualizing point transformation using the implemented perspective transformation and annotating edges for enhanced visualization in football AI.
Transforming Camera View to Peach View
Transforming points from the camera view to the football pitch view and visualizing the transformed points for accurate player tracking in football AI.
Visualizing Transformed Players and Points
Visualizing transformed players and points on the football pitch, incorporating filter criteria for improved visualization in football AI.
Wrapping Up Point Transformation
Exploring the visualization of transformed points and ensuring stability and accuracy in football AI player tracking.
Cleaning up Results and Trajectory Analysis
Cleaning up results for multiple ball detections and discussing trajectory analysis for improving accuracy in football AI.
Stability in Ball Tracking
Ensuring stability in ball tracking by averaging out homography matrices and addressing vibration issues in football AI.
Data Cleaning for Multiple Ball Detections
Handling cases of multiple ball detections and cleaning up results to maintain accuracy and reliability in football AI.
Visualizing Ball Trajectory
Visualizing the ball trajectory analysis and addressing issues with incorrect ball detections in football AI.
Trajectory Data Cleaning
Utilizing distance thresholds to clean up trajectory data and ensure accurate ball tracking in football AI.
Considerations for Football AI
Highlighting important considerations such as dataset expansion, occlusion handling, and frame rate optimization for effective football AI implementation.
Challenges in Perspective Transformation
Discussing challenges with homography in handling air objects and the need for 3D tracking for accurate trajectory estimation in football AI.
Key Point Detection Accuracy
Emphasizing the importance of key point detection accuracy in maintaining player positions and pitch visualizations in football AI.
Optimizing Model Efficiency
Addressing the need for optimizing model efficiency to meet real-time requirements in sports analytics and the methods to achieve faster processing speeds.
Future Exploration in Sports Analytics
Discussing areas for future exploration in sports analytics, including advanced player metrics and predictive modeling in football AI.
FAQ
Q: What are some of the key techniques discussed in the project for analyzing football games?
A: The project discusses techniques such as Sly embeddings, key detection, homography, ball trajectory analysis, Voronoi diagram, perspective transformation, object detection using YOLO V8, and tracking using unique Tracker IDs.
Q: How is player classification into teams achieved in the project?
A: Player classification into teams is achieved using embedding analysis, U-map for dimensionality reduction, and K-means clustering based on player positions to create a radar view showing player positions and ball movement.
Q: What is the significance of homography in the context of tracking player positions and ball movements in football AI?
A: Homography plays a crucial role in tracking player positions and ball movements by enabling perspective transformation, especially in situations where the camera is static, and ensuring accurate mapping of key points detected in the camera view to the football pitch view.
Q: How is keypoint detection used in the project for football AI?
A: Keypoint detection is utilized to identify characteristic points on the football field, such as player positions, goalposts, and referees, by training models on custom datasets and using Google Colab for training and inference.
Q: What are some challenges discussed regarding the use of homography in dynamic camera settings at sports events?
A: Challenges include maintaining accuracy in perspective transformation when the camera is in motion, handling cases of multiple ball detections, cleaning up trajectory data using distance thresholds, and addressing issues with incorrect ball detections.
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