A COMPARISON AND EVALUATION OF MOTION INDEXING ...

CHAPTER 2
PCA-BASED TECHNIQUES
In this section, the overview of motion segmentation and PCA is provided and
then the two techniques are described which use SVD as a tool to analyze the data and
segment it. These two techniques are refered using the research paper, Segmenting
motion capture data into distinct behaviors[3]. Both of these techniques traverse the
data creating segments of data. The motion is considered as a sequence of poses.
This sequence is segmented if there is a change in the distribution of data locally.
Ideally, high level behaviors, which are different verbs, are segmented out like walk,
climb and, jump. The first method uses Principal Component Analysis (PCA) to
segment the motion on the basis of the dimensionality change in local motion. The
second technique uses Probabilistic PCA to create a probabilistic distribution model
of a motion and segments the data based on this model. We also propose a novel
technique to perform indexing on the segmented data.
Motion Segmentation is a way of decomposing a long sequence of motion into
smaller subsequences based on different behaviors. The collection of long sequences
during motion capture and the posterior segmentation is an efficient way to capture
natural and realistic actions. Segmentation of motion data can be performed during
the capture or manually after the data is captured. As captured sequences become
longer in one session, the manual segmentation becomes time consuming. On the
other hand, automatic segmentation takes into account statistical properties of the
data to segment the motion automatically. Usually, a segmentation method builds a
mathematical model to analyze these statistical properties.
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