A hybrid graphical model for robust feature extraction from video

We consider a visual scene analysis scenario where objects (e.g. people, cars) pass through the viewing field of a static camera and need to be detected and segmented from the background. For this purpose, we introduce a hybrid dynamic Bayesian network and derive an expectation propagation (EP) algorithm for robust estimation of object shapes and appearance statistics. We demonstrate the viability of the approximation on an object detection task from real videos, where objects' smooth shapes are segmented from the background. The model is readily extendible to multi-object multi-camera scenarios and can be coupled in a transparent and consistent way with a hierarchical model for object identification under uncertainty.