Journal of Applied Mathematics
Volume 2013 (2013), Article ID 420286, 13 pages
Research Article

Spatial Object Tracking Using an Enhanced Mean Shift Method Based on Perceptual Spatial-Space Generation Model

Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received 10 January 2013; Accepted 20 March 2013

Academic Editor: Graziano Chesi

Copyright © 2013 Pengcheng Han 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.


Object tracking is one of the fundamental problems in computer vision, but existing efficient methods may not be suitable for spatial object tracking. Therefore, it is necessary to propose a more intelligent mathematical model. In this paper, we present an intelligent modeling method using an enhanced mean shift method based on a perceptual spatial-space generation model. We use a series of basic and composite graphic operators to complete signal perceptual transformation. The Monte Carlo contour detection method could overcome the dimensions problem of existing local filters. We also propose the enhanced mean shift method with estimation of spatial shape parameters. This method could adaptively adjust tracking areas and eliminate spatial background interference. Extensive experiments on a variety of spatial video sequences with comparison to several state-of-the-art methods demonstrate that our method could achieve reliable and accurate spatial object tracking.