Moeslund / Thomas / Hilton Computer Vision in Sports

1. Auflage 2015
ISBN: 978-3-319-09396-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 319 Seiten, eBook

Reihe: Advances in Computer Vision and Pattern Recognition

The first book of its kind devoted to this topic, this comprehensive text/reference presents state-of-the-art research and reviews current challenges in the application of computer vision to problems in sports. Opening with a detailed introduction to the use of computer vision across the entire life-cycle of a sports event, the text then progresses to examine cutting-edge techniques for tracking the ball, obtaining the whereabouts and pose of the players, and identifying the sport being played from video footage. The work concludes by investigating a selection of systems for the automatic analysis and classification of sports play. The insights provided by this pioneering collection will be of great interest to researchers and practitioners involved in computer vision, sports analysis and media production.

Zielgruppe

Research

Autoren/Hrsg.

Moeslund, Thomas B.

Thomas, Graham

Hilton, Adrian

Inhaltsverzeichnis

Introduction to the Use of Computer Vision in Sports.-
Part I: Where is the Ball?.-
Ball Tracking for Tennis Video Annotation.- Plane Approximation-Based Approach for 3D Reconstruction of Ball Trajectory for Performance Analysis in Table Tennis.- On-Field Testing and Evaluation of a Goal-Line Technology System.-
Part II: Where Are the Players?.-
Occlusion Detection via Structured Sparse Learning for Robust Object Tracking.- Detecting and Tracking Sports Players with Random Forests and Context-Conditioned Motion Models.- Geometry Reconstruction of Players for Novel-View Synthesis of Sports Broadcasts.- Estimating Athlete Pose from Monocular TV Sports Footage.-
Part III: What Are They Playing?.-
Action Recognition in Realistic Sports Videos.- Classi?cation of Sports Types Using Thermal Imagery.- Event-Based Sports Videos Classi?cation Using HMM Framework.-
Part IV: What's Going On?.-
Representing Team Behaviours from Noisy Data Using Player Role.- Recognizing Team Formation in American Football.- Real-Time Event Detection in Field Sport Videos.