Stochastic Tracking of Humans
Goal: 3D Human Motion
Overview
Collaborators
Why is it important?
Why is it hard?
Other Problems
Common Assumptions
Requirements
Bayesian Inference
Problems
Bayesian Formulation
Generative Model: Shape
Generative Model: Appearance
Appearance Model
Noise Model
Generative Model: Temporal
Robust Likelihood
Likelihood
Temporal Model: Smooth Motion
What does the posterior look like?
Particle Filtering
Representing the Posterior
Stochastic Search
Condensation
Visualizing Results
Arm Tracking: Smooth motion prior
Full-Body Tracking
Learning Temporal Models
Detecting Cycles
Modeling Cyclic Motion
Action-Specific Model
Temporal Model: Walking
Learned Walking Model
Stochastic 3D Tracking
No likelihood
Issues
Lessons Learned
Work to be done
Outlook
Related Work
Conclusions
Email: black@cs.brown.edu
Home Page: http://www.cs.brown.edu/people/black/