JOURNAL OF MULTIMEDIA (JMM)
ISSN : 1796-2048
Volume : 1 Issue : 3 Date : June 2006
Articulated Hand Motion Tracking Using ICA-based Motion Analysis and Particle Filtering
Makoto Kato, Yen-Wei Chen and Gang Xu
Full Text: PDF (1,243 KB)
This paper introduces a new representation of hand motions for tracking and recognizing
hand-finger gestures in an image sequence. A human hand has many joints, for example our hand
model has 15, and its high dimensionality makes it difficult to model hand motions. To make things
easier, it is important to represent a hand motion in a low dimensional space. Principle component
analysis (PCA) has been proposed to reduce the dimensionality. However, the PCA basis vectors
only represent global features, which are not optimal for representing intrinsic features. This paper
proposes an efficient representation of hand motions by independent component analysis (ICA).
The ICA basis vectors represent local features, each of which corresponds to the motion of a
particular finger. This representation is more efficient in modeling hand motions for tracking and
recognizing handfinger gestures in an image sequence. We will demonstrate the effectiveness of
the method by tracking a hand in real image sequences.
articulated hand tracking, principle component analysis, independent component analysis, particle
filtering, motion, gesture recognition.