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All talks will be posted as pre-recorded videos, availble throughout the conference week. Links for talks and Q&A meetings are available at the CVPR platform for registered attendees.<\/p>\n
Invited talks (videos available throughout the conference week):<\/strong><\/p>\n See titles and abstracts below.<\/p>\n <\/p>\n Oral presentations (videos available throughout the conference week):<\/strong><\/p>\n <\/p>\n Live sessions, June 25:<\/strong><\/p>\n 8.40-9.00 (PT) \/ 11.40-12.00 (EDT) \/ 17.40-18.00 (CET) : Live Q&A with Pascal Fua and Remo Ziegler<\/p>\n 10.00-10.30 (PT) \/ 13.00-13.30 (EDT) \/ 19.00-19.30 (CET): Live Q&A with paper authors<\/p>\n 11.10-11.30 (PT) \/ 14.10-14.30 (EDT) \/ 20.10-20.30 (CET): Live Q&A with Adri\u00e0 Arbu\u00e9s Sang\u00fcesa and Mehrsan Javan<\/p>\n 11.30-11.45 (PT) \/ 14.30-14.45 (EDT) \/ 20.30-20.45 (CET): Best paper award sponsored by Sportlogiq<\/p>\n Invited speakers<\/strong><\/p>\n \n\n<\/p>\n Pascal Fua, Professor, Computer Vision Laboratory, EPFL<\/strong><\/p>\n Title: Athletic 3D Pose Estimation<\/strong><\/p>\n Abstract: While supervised body pose estimation is rapidly becoming a mature field, the bottleneck remains the availability of sufficiently large training datasets, which cannot be guaranteed for many kinds of human motions. An effective way to address this is to leverage unsupervised data to learn a low-dimensional representation of poses. Then,\u00a0 it only takes very little annotated data to train a regressor to predict 3D poses from this representation.\u00a0In this talk I will discuss several classes of techniques that rely on unsupervised learning objectives that exploit the availability of multi-view footage and of video sequences for this purpose. I will demonstrate the benefits of this approach to track skiers, divers, and ball players.<\/p>\n Slides: Download<\/a><\/p>\n <\/p>\n Adri\u00e0 Arbu\u00e9s Sang\u00fcesa, PhD felllow, Universitat Pompeu Fabra<\/strong><\/p>\n Title: Bringing Computer Vision to the Court<\/strong><\/p>\n Abstract: Computer Vision is a really powerful tool to bring meaningful insights and statistics in the context of sport. However, it is sometimes difficult to implement the trained models in real-case scenarios, since there is a missing connection between research and the actual court. Stemming from a basketball baseline, this talk explains the complete pipeline that starts with data gathering, details\u00a0the creation of metrics and models, and ends up with communication and\u00a0implementation tips. By building this bridge between analysts and coaches and a smooth communication flow, the performance of teams can be potentially improved<\/p>\n Slides: Download<\/a><\/p>\n <\/p>\n Remo Ziegler, SVP Product Manager, Vizrt<\/strong><\/p>\n Title: AI opportunities in Sports Broadcast<\/strong><\/p>\n Abstract: The sports broadcast industry is a very exiting industry to work in. It is very fast paced and everybody tries to come up with new ways to excite the audience on the quest to maximize eyeballs. Additionally, to the fast paced nature of this industry, broadcasting is in the middle of a transition from linear TV to on-demand, customer centric driven content, which has an impact on the nature of production, automation, monetization and many more. In many of those steps understanding and visualizing data in a more automated fashion is key and thus the need for AI imminent. Let us take a glimpse at some of the challenges and opportunities of this field.<\/p>\n <\/p>\n Mehrsan Javan, CTO and co-founder, Sportlogiq<\/strong><\/p>\n Title: Sport Analytics: Turn Visual Data Into Insights<\/strong><\/p>\n Abstract: Sports analytics is about observing, understanding, and describing the game in an intelligent manner. In practice, most of the focus has been on visual perception to take the video data and extract tracking data and game events. However, turning the incomplete data into actionable insights for the clubs has always been a challenge. This talk focuses on the use of broadcast feed for sport analytics, covers the components of a vision system for data acquisition, provides examples of how Sportlogiq captures the data from broadcast videos and turns them into useful insights for the clubs to make better decisions<\/p>\n Slides: Download<\/a><\/p>\n Accepted papers<\/strong><\/p>\n Motivation<\/strong><\/p>\n Sports is said to be the social glue of society. It allows people to interact irrespective of their social status, age etc. With the rise of the mass media, a significant quantity of resources has been channeled into sports in order to improve understanding, performance and presentation. For example, areas like performance assessment, which were previously mainly of interest to coaches and sports scientists are now finding applications in broadcast and other media, driven by the increasing use of on-line sports viewing which provides a way of making all sorts of performance statistics available to viewers. Computer vision has recently started to play an important role in sports as seen in for example football where computer vision-based graphics in real-time enhances different aspects of the game. Computer vision algorithms have a huge potential in many aspects of sports ranging from automatic annotation of broadcast footage, through to better understand of sport injuries, and enhanced viewing. So far, the use of computer vision in sports has been scattered between different disciplines.<\/p>\n\n <\/p>\n\n Call for papers<\/strong><\/p>\n The ambition of this workshop is to bring together practitioners and researchers from different disciplines to share ideas and methods on current and future use of computer vision in sports. To this end we welcome computer vision-based research contributions as well as best-practice contributions focusing on the following (and similar) topics:<\/p>\n\n\n\n <\/p>\n\n Important dates<\/strong><\/p>\n\n\n\n <\/p>\n\n Submission instructions <\/strong><\/p>\n Policies and guidelines (same as for CVPR): Link<\/a><\/p>\n Submission:\u00a0Link<\/a><\/p>\n\n\n\n Accepted papers will be published in the CVPR workshop proceedings on IEEE Xplore and as open access on http:\/\/openaccess.thecvf.com\/.<\/p>\n\n <\/p>\n\n Best paper award<\/strong><\/p>\n 1000$ sponsored by Sportlogiq<\/a>.<\/p>\n\n\n\n <\/p>\n\n Program committee:<\/strong><\/p>\n Adrien Deliege, University of Liege, Belgium <\/p>\n\n Organizers<\/strong><\/p>\n Rikke Gade<\/a>, Aalborg University, Denmark <\/p>\n\n Previous editions of CVsports:<\/strong><\/p>\n\n\n\n <\/p>\n\n Related publications:<\/strong><\/p>\n\n\n\n <\/p>\n\n Overall Meeting Sponsors<\/strong> Workshop program All talks will be posted as pre-recorded videos, availble throughout the conference week. Links for talks and Q&A meetings are available at the CVPR platform for registered attendees. Invited talks (videos available throughout the conference week): Pascal Fua, Professor, Computer Vision Laboratory, EPFL. Remo Ziegler, SVP Product Manager, Vizrt. Adri\u00e0 Arbu\u00e9s Sang\u00fcesa, PhD […]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2023","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/pages\/2023","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/comments?post=2023"}],"version-history":[{"count":0,"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/pages\/2023\/revisions"}],"wp:attachment":[{"href":"https:\/\/vap.aau.dk\/cvsports\/wp-json\/wp\/v2\/media?parent=2023"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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March 7<\/del> March 14, 2021 (11.59pm Pacific Time)<\/li>\nApril 1<\/del>\u00a0April 7, 2021<\/li>\n
Anthony Cioppa, University of Liege, Belgium
Bastian Wandt, Leibniz University Hannover, Germany
Dan Gutfreund, IBM Research
Dan Mikami, NTT, Japan
Hideo Saito, Keio University, Japan
Jean-Yves Guillemaut, University of Surrey, UK
Jesse Davis, KU Leuven, Belgium
Jianhui Chen, University of British Columbia, Canada
Jonas Theiner, Universit\u00e4t Hannover, Germany
Li Cheng, University of Alberta, Canada
Marc Van Droogenbroeck, University of Liege, Belgium
Michael Jamieson, Sportlogiq, USA
Moritz Einfalt, University of Augsburg, Germany
Nicola Mosca, CNR ISSIA, Italy
Rainer Lienhart, Universitat Augsburg, Germany
Rajkumar Theagarajan, University of California, Riverside, USA
Sergio Escalera, Computer Vision Center (UAB) & University of Barcelona, Spain
Silvio Giancola, KAUST, Saudi Arabia
Simon Denman, Queensland University of Technology, Australia
Steven Schwarcz, University of Maryland College Park, USA<\/p>\n\n
Thomas Moeslund<\/a>, Aalborg University, Denmark
Graham Thomas<\/a>, BBC, UK
Adrian Hilton<\/a>, University of Surrey, UK
Jim Little<\/a>, University of British Columbia, Canada
Michele Merler<\/a>, IBM Research, USA<\/p>\n\n\n
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