Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game.
We describe the conceptual design, architecture, and implementation of a multimodal, robot-child dialogue system in a fast-paced, speech-controlled collaborative game.
We present initial findings from an experiment in which participants played Mafia, an established role-playing game, with our robot.
In this paper, we present a method which accurately estimates the likelihood of chances in soccer using strategic features from an entire season of player and ball tracking data taken from a professional league.
In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections.
In this paper, we give an overview of the types of analysis currently performed mostly with hand-labeled event data and highlight the problems associated with the influx of spatiotemporal data.
In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time.
In this paper, we use ball and player tracking data from STATS SportsVU from the 2012-2013 NBA season to analyze offensive and defensive formations of teams.
We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms.
In this study, we explored the impact of a co-located sidekick on child-robot interaction. We examined child behaviors while interacting with an expressive furniture robot and his robot lamp sidekick.
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