We present a solution for monitoring nocturnal giraffe behavior by reducing several hours of thermal camera surveillance footage into a short video summary which can be reviewed by experts.
We combine this information with tracked player positions to build a structured predictor. Given unseen player positions from a new game, we use the learned predictor to generate target pan-tilt-zoom values for a robotic camera.
We consider the problem of learning predictive models for in-game sports play prediction. Focusing on basketball, we develop models for anticipating near-future events given the current game state. We employ a latent factor modeling approach, which leads to a compact data representation that enables efficient prediction given raw spatiotemporal tracking data.
In this paper, given an entire season’s worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis.
In this paper, we use ball and player tracking data from “Hawk-Eye” to discover unique player styles and predict within-point events.
In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain.
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 show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.
Point-based targets, such as checkerboards, are often not practical for outdoor camera calibration, as cameras are usually at significant heights requiring extremely large calibration patterns on the ground.
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