Pose estimation and tracking using multivariate regression

Authors:
Arasanathan Thayananthan;Ramanan Navaratnam;Björn Stenger;Philip H. S. Torr;Roberto Cipolla
Affiliations:
University of Cambridge, Department of Engineering, Cambridge CB2 1PZ, UK;University of Cambridge, Department of Engineering, Cambridge CB2 1PZ, UK;Toshiba Cambridge Research Laboratory, Cambridge, CB4 0GZ, UK;Oxford Brookes University, Department of Computing, Wheatley, Oxford OX33 1HX, UK;University of Cambridge, Department of Engineering, Cambridge CB2 1PZ, UK
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

This paper presents an extension of the relevance vector machine (RVM) algorithm to multivariate regression. This allows the application to the task of estimating the pose of an articulated object from a single camera. RVMs are used to learn a one-to-many mapping from image features to state space, thereby being able to handle pose ambiguity.