Segmentation and Classification

Segmentation is accomplished by classification of each pixel into one of several models. The models consists of a static world modeled as a texture surface and a dynamic user modeled as a collection of gaussian blobs. The Mahalanobis distance to each class is computed for each pixel. This is a measure of the likelihood that a pixel can be explained by a class. Then pixels are assigned to the most likely class. This process produces a suppoert map:

This support map is then used to update the class models in a robust estimation framework.

Pfinder takes special care to identify and compensate for shadows cast by the user.

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