Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2013 (2013), Article ID 793013, 13 pages
Research Article

Memory-Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

College of Information and Control Engineering, China University of Petroleum, No. 66, Changjiang West Road, Economic and Technological Development Zone, Qingdao 266580, China

Received 28 March 2013; Accepted 28 April 2013

Academic Editors: P. Agarwal, S. Balochian, V. Bhatnagar, J. Yan, and Y. Zhang

Copyright © 2013 Yanjiang Wang 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.


The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods.