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Title: Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction

Abstract

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 trackingmore » are still limited. The method proposed in this paper not only expands upon the previous work but will also attempt to overcome these limitations.« less

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
800789
Report Number(s):
SAND2002-0801
TRN: US200224%%133
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 1 Apr 2002
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; ALGORITHMS; CALIBRATION; CAMERAS; COMPUTERS; DESIGN; IMAGE PROCESSING; PERFORMANCE; REAL TIME SYSTEMS; MAN-MACHINE SYSTEMS; REMOTE VIEWING EQUIPMENT; ROBOTS

Citation Formats

SMALL, DANIEL E., LUCK, JASON P., and CARLSON, JEFFREY J. Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction. United States: N. p., 2002. Web. doi:10.2172/800789.
SMALL, DANIEL E., LUCK, JASON P., & CARLSON, JEFFREY J. Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction. United States. doi:10.2172/800789.
SMALL, DANIEL E., LUCK, JASON P., and CARLSON, JEFFREY J. Mon . "Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction". United States. doi:10.2172/800789. https://www.osti.gov/servlets/purl/800789.
@article{osti_800789,
title = {Volumetric Video Motion Detection for Unobtrusive Human-Computer Interaction},
author = {SMALL, DANIEL E. and LUCK, JASON P. and CARLSON, JEFFREY J.},
abstractNote = {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.},
doi = {10.2172/800789},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Apr 01 00:00:00 EST 2002},
month = {Mon Apr 01 00:00:00 EST 2002}
}

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