Title :
Pose estimation using 3D view-based eigenspaces
Author :
Morency, Louis-Philippe ; Sundberg, Patrik ; Darrell, Trevor
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA, USA
Abstract :
We present a method for estimating the absolute pose of a rigid object based on intensity and depth view-based eigenspaces, built across multiple views of example objects of the same class. Given an initial frame of an object with unknown pose, we reconstruct a prior model for all views represented in the eigenspaces. For each new frame, we compute the pose-changes between every view of the reconstructed prior model and the new frame. The resulting pose-changes are then combined and used in a Kalman filter update. This approach for pose estimation is user-independent and the prior model can be initialized automatically from any viewpoint of the view-based eigenspaces. To track more robustly over time, we present an extension of this pose estimation technique where we integrate our prior model approach with an adaptive differential tracker. We demonstrate the accuracy of our approach on face pose tracking using stereo cameras.
Keywords :
Kalman filters; face recognition; image reconstruction; principal component analysis; solid modelling; tracking; 3D view-based eigenspaces; Kalman filter; adaptive differential tracker; face pose tracking; image reconstruction; pose estimation; principal component analysis; stereo cameras; Application software; Artificial intelligence; Cameras; Face detection; Head; Image reconstruction; Laboratories; Principal component analysis; Robustness; Stereo vision;
Conference_Titel :
Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on
Print_ISBN :
0-7695-2010-3
DOI :
10.1109/AMFG.2003.1240823