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.

Topics and features:

• Describes the latest research into ball tracking, addressing the challenges posed by the presence of occlusions and the use of only a small number of cameras
• Reviews various systems for player tracking and pose estimation
• Presents approaches for the improved generationof statistics and synthesis of virtual views
• Explores the "higher level" analysis of sports, from identifying types of sports to recognizing particular team behaviors based on multiple event or motion detections
• Discusses the detection of specific kinds of events for automatic highlights generation or searching of video archives

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.

Prof. Thomas B. Moeslund is Head of Media Technology, Aalborg, and Head of the Visual Analysis of People Lab at Aalborg University, Denmark. Dr. Graham Thomas leads the Immersive and Interactive Content team at BBC Research & Development, London, UK. Prof. Adrian Hilton is Director of the Centre for Vision, Speech and Signal Processing at the University of Surrey, Guildford, UK.

Inhalt

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.- Classication of Sports Types Using Thermal Imagery.- Event-Based Sports Videos Classication 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.