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Abstract: In recent years Sequential Monte Carlo (SMC) algorithms have been applied to capture the motion of humans. In this paper we apply a SMC algorithm to capture the motion of an articulated chain, e.g., a human arm, and show how the SMC algorithm can be improved in this context by applying auxiliary information. In parallel to a model-based approach we detect skin color blobs in the image as our auxiliary information and find the probabilities of each blob representing the hand. The probabilities... (Update)
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BibTeX entry: (Update)
@misc{ moeslund-motion,
author = "Thomas B. Moeslund and Erik Granum",
title = "Motion Capture of Articulated Chains by Applying Auxiliary Information
to the Sequential Monte Carlo Algorithm",
url = "citeseer.ist.psu.edu/713260.html" }
Citations (may not include all citations):406 Sequential Monte Carlo Methods in Practice (context) - Doucet, Freitas et al. - 2001
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102 ICONDENSATION: Unifying Lowlevel and High-level Tracking in .. - Isard, Blake - 1998
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81 A Multiple Hypothesis Approach to Figure Tracking - Cham, Rehg - 1999
61 A Survey of Computer Vision-Based Human Motion Capture - Moeslund, Granum - 2001
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17 Better Proposal Distributions: Object Tracking Using Unscent.. (context) - Rui, Chen - 2001
5 Tracking Humans from a Moving Platform - Davis, Philomin et al. - 2000
4 Technical Report CUEDFINFENG TR (context) - Freitas, Unscented et al. - 2000
3 Computer Vision-Based Motion Capture of Body Language (context) - Moeslund - 2003
3 Head Tracking by Active Particle Filtering (context) - Zeng, Ma - 2002
3 From Visual Tracking to Animation using Hierarchical Samplin.. (context) - Mitchelson, Hilton - 2003
1 Covarinace Scaled Sampling for Monocular 3D Body Tracking (context) - Sminchisescu, Triggs - 2001
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