Real-time 3D model-based motion tracking
A new model-based tracking algorithm is reported for real-time motion tracking per-formance. In this experiment, a single calibrated camera and geometric models are used. Each tracking cycle completes computation between a projected 3D model and the image. The early stage is to match and measure the...
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sg-ntu-dr.10356-389982023-07-04T16:02:55Z Real-time 3D model-based motion tracking Li, Zheng. Wang, Han School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation A new model-based tracking algorithm is reported for real-time motion tracking per-formance. In this experiment, a single calibrated camera and geometric models are used. Each tracking cycle completes computation between a projected 3D model and the image. The early stage is to match and measure the error between the model and corresponding image features; The later stage is to estimate the model pose according to previous measurement. The matching process includes two aspects of:(l)feature extraction using local minimum energy and (2)global matching of a known three-dimensional model against the projected features. A one-dimensional search strategy is adopted instead of local window operation. The feature energy which is defined as the negative absolute value of the edge strength, fits the small motion hypothesis. The algorithm reported here is robust to change in lighting and backgrounds. Master of Engineering 2010-05-21T03:39:25Z 2010-05-21T03:39:25Z 1997 1997 Thesis http://hdl.handle.net/10356/38998 en NANYANG TECHNOLOGICAL UNIVERSITY 116 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation Li, Zheng. Real-time 3D model-based motion tracking |
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A new model-based tracking algorithm is reported for real-time motion tracking per-formance. In this experiment, a single calibrated camera and geometric models are used. Each tracking cycle completes computation between a projected 3D model and the image. The early stage is to match and measure the error between the model and corresponding image features; The later stage is to estimate the model pose according to previous measurement. The matching process includes two aspects of:(l)feature extraction using local minimum energy and (2)global matching of a known three-dimensional model against the projected features. A one-dimensional search strategy is adopted instead of local window operation. The feature energy which is defined as the negative absolute value of the edge strength, fits the small motion hypothesis. The algorithm reported here is robust to change in lighting and backgrounds. |
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Wang, Han |
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Wang, Han Li, Zheng. |
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Theses and Dissertations |
author |
Li, Zheng. |
author_sort |
Li, Zheng. |
title |
Real-time 3D model-based motion tracking |
title_short |
Real-time 3D model-based motion tracking |
title_full |
Real-time 3D model-based motion tracking |
title_fullStr |
Real-time 3D model-based motion tracking |
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Real-time 3D model-based motion tracking |
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real-time 3d model-based motion tracking |
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2010 |
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http://hdl.handle.net/10356/38998 |
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1772829027298443264 |