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|Title: ||Human motion characterization and its applications in motion analysis and synthesis|
|Other Titles: ||Ren ti yun dong ke hua ji qi zai yun dong fen xi yu sheng cheng zhong de ying yong yan jiu|
|Authors: ||Qu, Huiyang (曲慧楊)|
|Department: ||Department of Computer Science|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2008|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Image processing -- Digital techniques.|
Human locomotion -- Computer simulation.
|Notes: ||CityU Call Number: TA1637 .Q25 2008|
xix, 205 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2008.
Includes bibliographical references (leaves 183-203)
|Abstract: ||Human motion analysis and synthesis are important but difficult problems in
computer vision and computer graphics. As the primary task in these fields, human
motion characterization is to extract information from human motion data and
represent in appropriate models. However, the complete recovery of motion
information is not always required. Statistical learning provides a tool for
characterization of human motion. In this thesis we focus on statistical learning based
human motion characterization methods for motion capture data in some applications
of human motion analysis and synthesis.
The first application is human motion classification. We treat the motion capture
data as a whole and extract the discriminative features by a proposed global subspace
analysis method named kernel clustering-based discriminant analysis (KCDA). KCDA
works by first mapping the original motion data into another high-dimensional space,
and then applying clustering-based discriminant analysis (CDA) in the transformed
space to extract features for discrimination. KCDA can combine the merits of both
kernel Fisher discriminant analysis (KFDA) and CDA to improve the classical Fisher
discriminant analysis (FDA) in the way that one hand multiple cluster structure of the
data is fully exploited and on the other hand kernel technique is imposed to get a
nonlinear separation hyperplane. In particular, the framework of KCDA integrates a
kernel fuzzy c-means algorithm (KFCM) to exploit the multiple cluster structure in
motion data and a fuzzy cluster ensemble to improve the stability and convergence of KFCM.
The second application is statistical modeling based human motion prediction. We
propose a genetic optimization algorithm to learn a graphical model which
characterizes the conditional independence between the body joints by representing the
joints as graph nodes and the relationships between the joints as graph edges. The
graphical model decomposes the high-dimensional joint probability distribution of all
the body joints into a number of low-dimensional distributions over small sets of the
joints. A subset of the body joints which provides predictions for the other joints is then
identified from the graphical model. The associations between the body joints are
learned through a set of multivariate relevance vector machines (RVM). The
performance of graphical model based human motion characterization is demonstrated
through experiments on motion prediction.
The last application field is human motion synthesis. The difficulty in realistic
human motion synthesis is due in part to the high dimensionality of the human motion.
However, most dynamic human behaviors are intrinsically low-dimensional with, for
example, legs and arms operating in a coordinated way. With this assumption, we
propose a statistical model to represent the dynamic properties of human motion. A
low-dimensional manifold is extracted through a nonlinear isometric feature mapping
(Isomap), and a set of dynamic models and the transition relationships between them
are learned from the manifold. Based on a nonlinear mapping function associating the
manifold with the original motion space, we exploit the cyclic patterns of locomotion
such as walking and running, learn dynamic models in segmented acyclic motion manifolds and synthesize new motion sequences.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b2340555|
|Appears in Collections:||CS - Doctor of Philosophy |
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