Data fusion for three-dimensional tracking using particle techniques

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2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number16401
Journal / PublicationOptical Engineering
Volume47
Issue number1
Publication statusPublished - 2008

Abstract

Robustness and tracking speed are two important indices for evaluating the performance of real-time 3-D tracking. We propose a new approach to fuse sensing data of the most current observation into a 3-D visual tracker with particle techniques. With the proposed data fusion method, the importance density function in the particle filter can be designed to represent posterior states by particle crowds in a better way. This makes the tracking system more robust to noise and outliers. On the other hand, because particle interpretation is performed in a much more efficient fashion, the number of particles used in tracking is greatly reduced, which improves the real-time performance of the system. Simulation and experimental results verified the effectiveness of the proposed method. © 2008 Society of Photo-Optical Instrumentation Engineers.

Research Area(s)

  • 3-D tracking, data fusion, importance density, particle filter