Table of Contents
Advances in Artificial Intelligence
Volume 2010, Article ID 175603, 15 pages
http://dx.doi.org/10.1155/2010/175603
Research Article

Multibandwidth Kernel-Based Object Tracking

Department of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran

Received 1 August 2009; Revised 2 December 2009; Accepted 20 April 2010

Academic Editor: Ce Zhu

Copyright © 2010 Aras Dargazany et al. 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

Object tracking using Mean Shift (MS) has been attracting considerable attention recently. In this paper, we try to deal with one of its shortcoming. Mean shift is designed to find local maxima for tracking objects. Therefore, in large target movement between two consecutive frames, the local and global modes are not the same as previous frames so that Mean Shift tracker may fail in tracking the desired object via localizing the global mode. To overcome this problem, a multibandwidth procedure is proposed to help conventional MS tracker reach the global mode of the density function using any staring points. This gradually smoothening procedure is called Multi Bandwidth Mean Shift (MBMS) which in fact smoothens the Kernel Function through a multiple kernel-based sampling procedure automatically. Since it is important for us to have less computational complexity for real-time applications, we try to decrease the number of iterations to reach the global mode. Based on our results, this proposed version of MS enables us to track an object with the same initial point much faster than conventional MS tracker.