Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/91016
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Type: Book chapter
Title: KSM based machine learning for markerless motion capture
Author: Tangkuampien, T.
Suter, D.
Citation: Machine learning for human motion analysis: theory and practice, 2009 / Wang, L., Cheng, L., Zhao, G. (ed./s), Ch.5, pp.74-106
Publisher: IGI Global
Issue Date: 2009
ISBN: 1605669008
9781605669007
Statement of
Responsibility: 
Therdsak Tangkuampien, David Suter
Abstract: A marker-less motion capture system, based on machine learning, is proposed and tested. Pose information is inferred from images captured from multiple (as few as two) synchronized cameras. The central concept of which, we call: Kernel Subspace Mapping (KSM). The images-to-pose learning could be done with large numbers of images of a large variety of people (and with the ground truth poses accurately known). Of course, obtaining the ground-truth poses could be problematic. Here we choose to use synthetic data (both for learning and for, at least some of, testing). The system needs to generalizes well to novel inputs:unseen poses (not in the training database) and unseen actors. For the learning we use a generic and relatively low fidelity computer graphic model and for testing we sometimes use a more accurate model (made to resemble the first author). What makes machine learning viable for human motion capture is that a high percentage of human motion is coordinated. Indeed, it is now relatively well known that there is large redundancy in the set of possible images of a human (these images form som sort of relatively smooth lower dimensional manifold in the huge dimensional space of all possible images) and in the set of pose angles (again, a low dimensional and smooth sub-manifold of the moderately high dimensional space of all possible joint angles). KSM, is based on the KPCA (Kernel PCA) algorithm, which is costly. We show that the Greedy Kernel PCA (GKPCA) algorithm can be used to speed up KSM, with relatively minor modifications. At the core, then, is two KPCA’s (or two GKPCA’s) - one for the learning of pose manifold and one for the learning image manifold. Then we use a modification of Local Linear Embedding (LLE) to bridge between pose and image manifolds.
Rights: Copyright © 2010, IGI Global
RMID: 0030007579
DOI: 10.4018/978-1-60566-900-7.ch005
Appears in Collections:Computer Science publications

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