Monthly Archives: June 2012

MatchPad: Interactive Glyph-Based Visualization for Real-Time Sports Performance Analysis

Today real-time sports performance analysis is a crucial aspect of matches in many major sports. For example, in soccer and rugby, team analysts may annotate videos during the matches by tagging specific actions and events, which typically result in some summary statistics and a large spreadsheet of recorded actions and events. To a coach, the summary statistics (e.g., the percentage of ball possession) lacks sufficient details, while reading the spreadsheet is time-consuming and making decisions based on the spreadsheet in real-time is thereby impossible. In this paper, we present a visualization solution to the current problem in real-time sports performance analysis. We adopt a glyph-based visual design to enable coaching staff and analysts to visualize actions and events “at a glance”. We discuss the relative merits of metaphoric glyphs in comparison with other types of glyph designs in this particular application. We describe an algorithm for managing the glyph layout at different spatial scales in interactive visualization. We demonstrate the use of this technical approach through its application in rugby, for which we delivered the visualization software, MatchPad, on a tablet computer. The MatchPad was used by the Welsh Rugby Union during the Rugby World Cup 2011. It successfully helped coaching staff and team analysts to examine actions and events in detail whilst maintaining a clear overview of the match, and assisted in their decision making during the matches. It also allows coaches to convey crucial information back to the players in a visually-engaging manner to help improve their performance.

Phil A. Legg, David H. S. Chung, Matthew L. Parry, Mark W. Jones, Rhys Long, Iwan W. Griffiths and Min Chen.
Eurovis 2012, Computer Graphics Forum 31(3), 1255-1264, 2012. [doi] [BibTeX]

Visualization of Large, Time-Dependent, Abstract Data with Integrated Spherical and Parallel Coordinates

Parallel Coordinates with Spherical axisParallel coordinates is one of the most popular and widely used visualization techniques for large, high dimensional data. Often, data attributes are visualized on individual axes with polylines joining them. However, some data attributes are more naturally represented with a spherical coordinate system. We present a novel coupling of parallel coordinates with spherical coordinates, enabling the visualization of vector and multi-dimensional data. The spherical plot is integrated as if it is an axis in the parallel coordinate visualization. This hybrid visualization benefits from enhanced visual perception, representing vector data in a more natural spatial domain and also reducing the number of parallel axis within the parallel coordinates plot. This raises several challenges which we discuss and provide solutions to, such as, visual clutter caused by over plotting and the computational complexity of visualizing large abstract, time-dependent data. We demonstrate the results of our work-in-progress visualization technique using biological animal tracking data of a large, multi-dimensional, time-dependent nature, consisting of tri-axial accelerometry samples as well as several additional attributes. In order to understand marine wildlife behavior, the acceleration vector is reconstructed in spherical coordinates and visualized alongside with the other data attributes to enable exploration, analysis and presentation of marine wildlife behavior.

James Walker, Zhao Geng, Mark W. Jones, Robert S. Laramee.
Eurovis Short Papers, 43-47, 2012. [doi] [BibTex]

Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination

In this paper we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a filtering strategy over path space, handling each subspace using specialised derivations of path tracing and progressive photon mapping. Evaluating diffuse paths with path tracing allows the use of sample strati cation over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an efficient, low noise progressive algorithm with vanishing bias combining the advantages of both Monte Carlo methods, and particle tracing.

Powerpoint iconIan C. Doidge, Mark W. Jones and Ben Mora.
CGI 2012, The Visual Computer 28(6-8), 603-612, 2012: The final publication is available at [doi] [BibTeX]