For object classes that exhibit large within-class variations, detection (segmentation) and pose (parameter) estimation can be chicken-egg problems. The main goal of this project is to develop algorithms for simultaneous detection, parameter estimation, and tracking of objects that exhibit high variability. The project focus is on three areas: (1) methods for dimensionality reduction that incorporate knowledge of object dynamics, (2) models that combine a collection of simpler local models to efficiently and accurately approximate nonlinear motion dynamics in a state-based model for tracking, (3) algorithms that can detect an instance of the object class in the image, and at the same time estimate the object's parameters. More details can be found here.

This web page describes research that is supported by the National Science Foundation, through grant 0713168. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.