Table of Contents
ISRN Signal Processing
Volume 2011, Article ID 173176, 15 pages
http://dx.doi.org/10.5402/2011/173176
Research Article

Object Modelling and Tracking in Videos via Multidimensional Features

School of Computing and Mathematics, University of Western Sydney, NSW 1797, Australia

Received 30 November 2010; Accepted 5 January 2011

Academic Editor: C. S. Lin

Copyright © 2011 Zhuhan Jiang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the previous frame and the posterior achieved via the current pixel distribution of the object. Consideration has also been made to a number of relevant aspects of object tracking including multidimensional features and the mixture of colours, textures, and object motion. The experiment of the proposed method on the video sequences has been conducted and has shown its effectiveness in capturing the target in a moving background and with nonrigid object motion.