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Computer Vision in Sports Softcover reprint of the original 1st ed. 2014 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 319 pages, height x width: 235x155 mm, weight: 5641 g, 139 Illustrations, color; 32 Illustrations, black and white; XI, 319 p. 171 illus., 139 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Computer Vision and Pattern Recognition
  • Izdošanas datums: 07-Oct-2016
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319379739
  • ISBN-13: 9783319379739
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Computer Vision in Sports Softcover reprint of the original 1st ed. 2014Palielināt
  • Formāts: Paperback / softback, 319 pages, height x width: 235x155 mm, weight: 5641 g, 139 Illustrations, color; 32 Illustrations, black and white; XI, 319 p. 171 illus., 139 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Computer Vision and Pattern Recognition
  • Izdošanas datums: 07-Oct-2016
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319379739
  • ISBN-13: 9783319379739
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319379739.html
Keywords:
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.

Introduction to the Use of Computer Vision in SportsGraham Thomas, Thomas B. Moeslund and Adrian HiltonPart I: Where is the Ball?Ball Tracking for Tennis Video AnnotationFei Yan, William Christmas and Josef KittlerPlane Approximation-Based Approach for 3D Reconstruction of Ball Trajectory for Performance Analysis in Table TennisSho Tamaki and Hideo SaitoOn-Field Testing and Evaluation of a Goal-Line Technology SystemPaolo Spagnolo, Pier Luigi Mazzeo, Marco Leo, Massimiliano Nitti, Ettore Stella and Arcangelo DistantePart II: Where Are the Players?Occlusion Detection via Structured Sparse Learning for Robust Object TrackingTianzhu Zhang, Bernard Ghanem, Changsheng Xu and Narendra AhujaDetecting and Tracking Sports Players with Random Forests and Context-Conditioned Motion ModelsJingchen Liu and Peter CarrGeometry Reconstruction of Players for Novel-View Synthesis of Sports BroadcastsTiberiu Popa, Marcel Germann, Remo Ziegler, Richard Keiser and Markus Gross

Estimating Athlete Pose from Monocular TV Sports FootageMykyta Fastovets, Jean-Yves Guillemaut and Adrian HiltonPart III: What Are They Playing?Action Recognition in Realistic Sports VideosKhurram Soomro and Amir Roshan ZamirClassi cation of Sports Types Using Thermal ImageryRikke Gade and Thomas B. MoeslundEvent-Based Sports Videos Classi cation Using HMM FrameworkShyju Wilson, C. Krishna Mohan and K. Srirama MurthyPart IV: What"s Going On?Representing Team Behaviours from Noisy Data Using Player RoleAlina Bialkowski, Patrick Lucey, Peter Carr, Sridha Sridharan and Iain MatthewsRecognizing Team Formation in American FootballIndriyati Atmosukarto, Bernard Ghanem, Mohamed Saadalla and Narendra AhujaReal-Time Event Detection in Field Sport VideosRafal Kapela, Kevin McGuinness, Aleksandra Swietlicka and Noel E. O"Connor

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.