CVPR 2011 workshop on gesture
recognition
Learning Structured Models for Recognizing
Human Actions
Greg Mori
Simon Fraser
University, Canada
The development
of automatic methods for recognizing human actions is a challenging
computer vision problem. Robust solutions to this problem would
facilitate a variety of applications from image retrieval to improving
safety in assisted living facilities. In this talk I will present
work towards solving this problem via the learning of structured
models. I will describe a model that uses a hidden Conditional
Random Field (hCRF) to learn a representation for motion parts in
conjunction with whole-body templates. Second, a variant of this
model is used for treating body pose as a latent variable for action
recognition. Finally, I will describe a model relating the
actions of individuals and groups of people to provide context for
action recognition. A latent variable framework that adapts the
latent connections between actions of individuals will be presented.
This is joint
work with Tian Lan, Yang Wang, and Weilong Yang.