Kernel-based high-dimensional histogram estimation for visual tracking

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 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 show that the proposed method outperforms both full intensity histogram mean-shift and global covariance searches.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Allen Tannenbaum, James G. Malcolm, Peter Karasev
• Keywords: Distribution Parameters, Global Covariance Searches, Histogram Representations, ICIP 2008, Image Processing, Intensity Histogram Mean-shift, Method Outperforms