PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate Bayesian methods for kernel-based object tracking
Zoran Zivkovic, Ali Taylan Cemgil and Ben Kröse
Computer Vision and Image Understanding Volume 113, Number 6, pp. 743-749, 2009.

Abstract

A framework for real-time tracking of complex non-rigid objects is presented. The object shape is approximated by an ellipse and its appearance by histogram based features derived from local image properties. An efficient search procedure is used to find the image region with a histogram most similar to the histogram of the tracked object. The procedure is a natural extension of the mean-shift procedure with Gaussian kernel which allows handling the scale and orientation changes of the object. The presented procedure is integrated into a set of Bayesian filtering schemes. We compare the regular and mixture Kalman filter and other sequential importance sampling (particle filtering) techniques.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6603
Deposited By:Christof Monz
Deposited On:08 March 2010