Kernel-based high-dimensional histogram estimation for visual tracking
Author(s)
Karasev, Peter
Malcolm, James G.
Tannenbaum, Allen R.
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters such as mixed moments provides a significantly reduced size representation. The discriminating power is comparable to that of the corresponding full high dimensional histogram yet at far less spatial and computational complexity. The proposed method is robust in the presence of noise and illumination changes, and provides a natural extension to the use of mixture models. Experiments demonstrate that the proposed method outperforms both full color mean-shift and global covariance searches.
Sponsor
Date
2008-10
Extent
Resource Type
Text
Resource Subtype
Proceedings