1,160 research outputs found
Augmenting Sports Videos with VisCommentator
Visualizing data in sports videos is gaining traction in sports analytics,
given its ability to communicate insights and explicate player strategies
engagingly. However, augmenting sports videos with such data visualizations is
challenging, especially for sports analysts, as it requires considerable
expertise in video editing. To ease the creation process, we present a design
space that characterizes augmented sports videos at an element-level (what the
constituents are) and clip-level (how those constituents are organized). We do
so by systematically reviewing 233 examples of augmented sports videos
collected from TV channels, teams, and leagues. The design space guides
selection of data insights and visualizations for various purposes. Informed by
the design space and close collaboration with domain experts, we design
VisCommentator, a fast prototyping tool, to eases the creation of augmented
table tennis videos by leveraging machine learning-based data extractors and
design space-based visualization recommendations. With VisCommentator, sports
analysts can create an augmented video by selecting the data to visualize
instead of manually drawing the graphical marks. Our system can be generalized
to other racket sports (e.g., tennis, badminton) once the underlying datasets
and models are available. A user study with seven domain experts shows high
satisfaction with our system, confirms that the participants can reproduce
augmented sports videos in a short period, and provides insightful implications
into future improvements and opportunities
Algorithms for the Analysis of Spatio-Temporal Data from Team Sports
Modern object tracking systems are able to simultaneously record trajectories—sequences of time-stamped location points—for large numbers of objects with high frequency and accuracy. The availability of trajectory datasets has resulted in a consequent demand for algorithms and tools to extract information from these data. In this thesis, we present several contributions intended to do this, and in particular, to extract information from trajectories tracking football (soccer) players during matches. Football player trajectories have particular properties that both facilitate and present challenges for the algorithmic approaches to information extraction. The key property that we look to exploit is that the movement of the players reveals information about their objectives through cooperative and adversarial coordinated behaviour, and this, in turn, reveals the tactics and strategies employed to achieve the objectives. While the approaches presented here naturally deal with the application-specific properties of football player trajectories, they also apply to other domains where objects are tracked, for example behavioural ecology, traffic and urban planning
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