Abstract
Human motion analysis is a challenging research area aimed at automating the study of human behavior. An important part of any such system is the component that performs the Human Motion Capture (HMC); in order for human motion to be processed and semantically analyzed, a mathematical representation of the observed motion needs to be extracted. There are two separate aspects to a HMC system; sensing (hardware) and processing (software). The processing itself comprises of an initialization (anthropometry and pose estimation) and a tracking phase. In this chapter, we present methods for three-dimensional model-based human motion capture from uncalibrated passive optical sensors with semi-antomatic initialization and tracking. Such methods allow for non-untrusive capture of natural human behavior from video cameras or from archival recordings. We demonstrate the accuracy, advantages, and limitations of our methods for various classes of data.
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© 2006 Springer Science+Business Media, Inc.
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Kakadiaris, I., Barrón, C. (2006). Model-Based Human Motion Capture. In: Paragios, N., Chen, Y., Faugeras, O. (eds) Handbook of Mathematical Models in Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/0-387-28831-7_20
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DOI: https://doi.org/10.1007/0-387-28831-7_20
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-26371-7
Online ISBN: 978-0-387-28831-4
eBook Packages: Computer ScienceComputer Science (R0)