Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction

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The computer vision field has undergone a revolution of sorts in the past five years. Moore's law has driven real-time image processing from the domain of dedicated, expensive hardware, to the domain of commercial off-the-shelf computers. This thesis describes their work on the design, analysis and implementation of a Real-Time Shape from Silhouette Sensor (RT S{sup 3}). The system produces time-varying volumetric data at real-time rates (10-30Hz). The data is in the form of binary volumetric images. Until recently, using this technique in a real-time system was impractical due to the computational burden. In this thesis they review the previous ... continued below

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54 pages

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SMALL, DANIEL E.; LUCK, JASON P. & CARLSON, JEFFREY J. April 1, 2002.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

The computer vision field has undergone a revolution of sorts in the past five years. Moore's law has driven real-time image processing from the domain of dedicated, expensive hardware, to the domain of commercial off-the-shelf computers. This thesis describes their work on the design, analysis and implementation of a Real-Time Shape from Silhouette Sensor (RT S{sup 3}). The system produces time-varying volumetric data at real-time rates (10-30Hz). The data is in the form of binary volumetric images. Until recently, using this technique in a real-time system was impractical due to the computational burden. In this thesis they review the previous work in the field, and derive the mathematics behind volumetric calibration, silhouette extraction, and shape-from-silhouette. For the sensor implementation, they use four color camera/framegrabber pairs and a single high-end Pentium III computer. The color cameras were configured to observe a common volume. This hardware uses the RT S{sup 3} software to track volumetric motion. Two types of shape-from-silhouette algorithms were implemented and their relative performance was compared. They have also explored an application of this sensor to markerless motion tracking. In his recent review of work done in motion tracking Gavrila states that results of markerless vision based 3D tracking are still limited. The method proposed in this paper not only expands upon the previous work but will also attempt to overcome these limitations.

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54 pages

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  • Other Information: PBD: 1 Apr 2002

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  • Report No.: SAND2002-0801
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/800789 | External Link
  • Office of Scientific & Technical Information Report Number: 800789
  • Archival Resource Key: ark:/67531/metadc738377

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • April 1, 2002

Added to The UNT Digital Library

  • Oct. 19, 2015, 7:39 p.m.

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  • April 12, 2016, 1:13 p.m.

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SMALL, DANIEL E.; LUCK, JASON P. & CARLSON, JEFFREY J. Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction, report, April 1, 2002; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc738377/: accessed November 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.