Machine Learning & Data Analytics

We describe a novel approach for computing collision-free global trajectories for p agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM) algorithm. Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments.

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.