Active Hidden Models for Tracking with Kernel Projections
Date
2009-03-10
DOI
Authors
Epstein, Samuel
Betke, Margrit
Version
OA Version
Citation
Epstein, Samuel; Betke, Margrit. "Active Hidden Models for Tracking with Kernel Projections", Technical Report BUCS-TR-2009-006, Computer Science Department, Boston University, March 10, 2009. [Available from: http://hdl.handle.net/2144/1730]
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.