Machine Learning and Optimization

We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition.

We develop a new scalable and accurate incremental object detection algorithm, based on several extensions of large-margin embedding (LME)

In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections.

Player movements in team sports are often complex and highly correlated with both nearby and distant players. A single motion model would require many degrees of freedom to represent the full motion diversity of each player and could be difficult to use in practice. I

We present Mole Madness, a side-scrolling computer game that is built to explore multi-child language use, turn-taking, engagement, and social interaction in a fast-paced speech-operated activity.

In this work, we review autonomous camera systems developed over the past twenty years.

We introduce a set of Game Context Features extracted from noisy detections to describe the current state of the match, such as how the players are spatially distributed.

In this paper, we present an unimodal interface concept that allows one person to cover live sporting action by controlling multiple cameras and and determining which view to broadcast.