Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2013, Special Issue (2013), Article ID 420286, 13 pages.

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

Pengcheng Han, Junping Du, and Ming Fang

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 420286, 13 pages.

Dates
First available in Project Euclid: 9 May 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1399645345

Digital Object Identifier
doi:10.1155/2013/420286

Mathematical Reviews number (MathSciNet)
MR3045381

Zentralblatt MATH identifier
1266.68184

Citation

Han, Pengcheng; Du, Junping; Fang, Ming. Spatial Object Tracking Using an Enhanced Mean Shift Method Based on Perceptual Spatial-Space Generation Model. J. Appl. Math. 2013, Special Issue (2013), Article ID 420286, 13 pages. doi:10.1155/2013/420286. https://projecteuclid.org/euclid.jam/1399645345


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