Thursday 1st March, 2012
11:30am to 12:10pm
Our presentation will cover the nascent fusion of automatically-collected live Digital Records of sports Events (DREs) with Augmented Reality (AR), primarily for television broadcast.
AR has long been used to in broadcast sports to show elements of the event that are otherwise difficult to see – the canonical examples are the virtual yellow “1st and 10” line for American Football and ESPNs KZone™ strike zone graphics. Similarly, sports leagues and teams have historically collected large amounts of data on events, often expending huge amounts of manual effort to do so. Our talk will discuss the evolution of data-driven AR graphics and the systems that make them possible. We’ll focus on systems for automating the collection of huge amounts of event data/metadata, such as the race car tracking technology used by NASCAR and the MLB’s PitchFX™ ball tracking system. We provide a rubric for thinking about classes of sports event data that encompasses scoring, event and action semantics metadata, and participant motion.
We’ll briefly discuss the history of these sports data collection technologies, and then take a deeper look at how the current first generation of automated systems are being leveraged for increasingly sophisticated analyses and visualizations, often via AR, but also through virtual worlds renderings from viewpoints unavailable or impossible from broadcast cameras. The remainder of the talk will examine two case studies highlighting the interplay between rich, live sports data and augmented reality visualization.
The first case study will describe one of the first of the next-gen digital records systems to come online and track players – Sportvision’s FieldFX™ system for baseball. Although exceeding difficult to collect, the availability of robust player motion data promises to revolutionize areas such as coaching and scouting performance analysis, fantasy sports and wagering, broadcast TV graphics and commentary, and sports medicine. We’ll show examples of some potential applications, and also cover data quality challenges in some detail, in order to examine the impact that these challenges have on the applications using the data.
The second case study will examine the rise of automated DRE collection as an answer to that nagging question about AR – ‘what sort of things do people want to see that way?’ Many of the latest wave of AR startups are banking huge amounts of venture money that the answer is in user-generated or crowd-sourced content. While this may end up being true for some consumer-focused mobile applications, our experience in the notoriously tight-fisted rights and monetization environment of sports has led directly to the requirement to create owned, curated data sources. This came about from four realizations that we think are more generally applicable to AR businesses…
Cool looking isn’t a business, even in sports.
It must be best shown in context, over video, or it won’t be shown at all.
The ability to technically execute AR is no longer a barrier to entry. Cutting edge visualization will only seem amazing for the next six seconds.
We established impossibly high quality expectations, and now the whole industry has to live with them.
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