3D Camera Calibration of Sports Videos Using Physical Guidance

Project Overview

In recent years, technologies like goal-line systems have revolutionized sports, providing accurate and reliable solutions for refereeing, game analysis, and athlete performance evaluation. Many of these innovations depend on extracting 3D information from 2D images or videos, a process that relies heavily on precise camera calibration. This thesis project aims to optimize the camera calibration process, especially in the context of sports videos, by leveraging physical knowledge.

Motivation

A well-calibrated camera is essential for accurately interpreting 3D information from a scene. However, obtaining accurate camera calibration matrices can be challenging. Traditional approaches often rely on:

• Multi-view camera systems.

• Videos captured with a moving camera in a static environment.

• Known 3D positions of key points in the image.

While solving for camera matrices from known 2D-3D point pairs is straightforward, it can become numerically unstable when the detected 2D points are noisy. In this project, we aim to enhance camera calibration for sports videos by incorporating physical knowledge of ball dynamics. By analyzing the ball's trajectory in image space, we can refine the calibration matrices, yielding significantly more stable and reliable estimates.

What You Will Do

• Deep Learning: Implement and use neural networks for keypoint detection and semantic segmentation.

• Numerical Optimization: Improve calibration accuracy through advanced optimization techniques.

• Physics Integration: Utilize differential equations to model and analyze the ball’s motion.

• Real-World Application: Work with broadcast sports videos and develop an evaluation benchmark tailored to the proposed calibration method.

• Publication Potential: If the results are promising, there will be an opportunity to publish your findings.

Don’t worry—you will receive guidance and basic implementations to support your work.

Required Background and Skills

• Strong Foundations: Knowledge of camera matrices and coordinate transformations, as covered in the Grundlagen der Signalverarbeitung und des Maschinellen Lernens lecture, is highly recommended.

• Comfort with Math and Physics: While a background in physics is not mandatory, familiarity with relevant equations and numerical methods is beneficial.

Why Join This Project?

• Gain hands-on experience with cutting-edge technologies in computer vision and machine learning.

• Solve real-world challenges in sports video analysis.

• Work on a project with high potential for academic publication and practical impact.

Interested? Let’s refine sports technology together!

For more information, contact Daniel Kienzle

The access to masks for objects in images is of great importance to many computer vision tasks. Manually annotating such object masks (for example with polygon drawings), however, takes an extensive amount of time. In addition to this, the annotation of finely jagged edges and delicate structures poses a considerable problem. Interactive segmentation systems try to drastically ease this task by using forms of user guidance that can be annotated cheaply in order to predict an object mask. Usually this guidance takes the form of right/left mouse clicks to annotate single background/foreground pixels.

Semantic segmentation constitutes the task of classfiying every single pixel into one of several predefined classes. In consequence interactive segmentation systems constitute a combination of the two tasks: The segmentation happens on the basis of user guidance while the goal is to circumvent a costly annotation process. Instead of annotating single objects, the goal is to divide the entire input image into several class surfaces.

Literature:

[1] : https://ceur-ws.org/Vol-2766/paper1.pdf

[2] : https://arxiv.org/abs/2003.14200

If case of interest, contact Robin Schön (robin.schoen@uni-a.de)