The Observation Model

The low-level features extracted from video comprise the final element of our system. Our system tracks regions that are visually similar, and spatially coherent: blobs. We can represent these 2-D regions by their low-order statistics. Clusters of 2-D points have 2-D spatial means and covariance matrices, which we shall denote ${\mbox{\boldmath$\ \mu $}}$ and ${\mbox{\boldmath$\ \Sigma $} }$. The blob spatial statistics are described in terms of their second-order properties; for computational convenience we will interpret this as a Gaussian model:

$$\Pr({\bf O} \vert {\mbox{\boldmath$ \mu $}}_k, {\mbox{\bold... ...\vert{\mbox{\boldmath$ \Sigma $}}_k\right\vert}^{\frac{1}{2}}}$$ (2.7)

The Gaussian interpretation is not terribly significant, because we also keep a pixel-by-pixel support map showing the actual occupancy [24].

These 2-D features are the input to the 3-D blob estimation equation used by Azarbayejani and Pentland [1]. This observation equation relates the 2-D distribution of pixel values to a tracked object's 3-D position and orientation.

These observations supply constraints on the underlying 3-D human model. Due to their statistical nature, observations are easily modeled as soft constraints. Observations are integrated into the dynamic evolution of the system by modeling them as descriptions of potential fields, as discussed in Section 2.2.1.