Technical Report BU-CS-2009-006

Title: Camera-based Interfaces and Assistive Software for People with
       Severe Motion Impairments

Author: Samuel Epstein and Margrit Betke

Date:  May 2009


Abstract:

We introduce Active Hidden Models (AHM) that utilize kernel methods
traditionally associated with classification.  We use AHMs to track
deformable objects in video sequences by leveraging kernel
projections.  We introduce the ``subset projection'' method which
improves the efficiency of our tracking approach by a factor of ten.
We successfully tested our method on facial tracking with extreme head
movements (including full 180-degree head rotation), facial
expressions, and deformable objects.  Given a kernel and a set of
training observations, we derive unbiased estimates of the accuracy of
the AHM tracker.  Kernels are generally used in classification methods
to make training data linearly separable.  We prove that the optimal
(minimum variance) tracking kernels are those that make the training
observations linearly dependent.