no code implementations • 3 Feb 2021 • Will Gürpınar-Morgan, Daniel Dinsdale, Joe Gallagher, Aditya Cherukumudi, Patrick Lucey
The ability to predict what shot a batsman will attempt given the type of ball and match situation is both one of the most challenging and strategically important tasks in cricket.
no code implementations • 30 Dec 2019 • Jennifer Hobbs, Matthew Holbrook, Nathan Frank, Long Sha, Patrick Lucey
Central to all machine learning algorithms is data representation.
no code implementations • 16 Oct 2019 • Matthew Holbrook, Jennifer Hobbs, Patrick Lucey
Sporting events are extremely complex and require a multitude of metrics to accurate describe the event.
no code implementations • ECCV 2018 • Panna Felsen, Patrick Lucey, Sujoy Ganguly
Simultaneously and accurately forecasting the behavior of many interacting agents is imperative for computer vision applications to be widely deployed (e. g., autonomous vehicles, security, surveillance, sports).
2 code implementations • ICLR 2019 • Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay.
no code implementations • NeurIPS 2016 • Stephan Zheng, Yisong Yue, Patrick Lucey
We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations.
no code implementations • ICML 2017 • Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey
We study the problem of imitation learning from demonstrations of multiple coordinating agents.
no code implementations • 15 Sep 2016 • Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan
In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories.
no code implementations • NeurIPS 2015 • Mathew Monfort, Brenden M. Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum
Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself.
no code implementations • CVPR 2013 • Patrick Lucey, Alina Bialkowski, Peter Carr, Stuart Morgan, Iain Matthews, Yaser Sheikh
In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain.