Abstract for thayananthan_eccv06

Proc. European Conference on Computer Vision 2006.


Arasanathan Thayananthan, Ramanan Navaratnam, Bjorn Stenger, Philip H. S. Torr, Roberto Cipolla

May 2006

This paper presents a learning based approach to tracking articulated human body motion from a single camera. In order to address the problem of pose ambiguity, a one-to-many mapping from image features to state space is learned using a set of relevance vector machines, extended to handle multivariate outputs. The image features are Hausdorff matching scores obtained by matching different shape templates to the image, where the multivariate relevance vector machines (MVRVM) select a sparse set of these templates. We demonstrate that these Hausdorff features reduce the estimation error in clutter compared to shape-context histograms. The method is applied to the pose estimation problem from a single input frame, and is embedded within a probabilistic tracking framework to include temporal information. We apply the algorithm to 3D hand tracking and full human body tracking.

(ftp:) thayananthan_eccv06.pdf (http:) thayananthan_eccv06.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.