Articulated Hand Tracking by PCA-ICA Approach

Summary:

Kato et al. used Independent Component Analysis (ICA) to find basis vectors for hand motion features. The authors first use Principle Components Analysis to reduce the dimensionality of their system, and then they use ICA to find a set of vectors that are statistically independent from each other (i.e., basis vectors).

Data on 20 angles was collected with a glove. The authors then sandwiched all of the data for the 20 sensors together into one large vector; each sensor was sampled across 100 time points, and the data from all 20 sensors was merged into a 2000-dimension vector.

ICA is used to find the basis vectors for a hand such that a linear combination of these vectors will produce a desired hand movement. The basis vectors U are found through a weight matrix W and a sample of motion data X (where X is a matrix of hand motions). A neural learning algorithm (in this case, gradient descent) is used to calculate the weights. The resulting 5 basis vectors are the movement of each finger individually.

The authors then deviated from their abstract and discussed actually tracking a hand using particle filtering. A hand's current position can be estimated from its prior positions, so each basis vector can estimate where it believes the finger will be given its prior positions. The authors also segment a hand out of an image by doing some thresholding on an image and overlaying a hand model in the image to find the hand location.

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Discussion:

There are no results.
The basis vectors seem obvious, but I'm glad that ICA found them.
There are no results.