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Reference features can either be sampled from the base shape and appearance model (off-line features) or from image data (online features), and subsequently back-projected onto the model surface so that they can be reprojected at different hypotheses and used for matching and pose estimation The two sampling procedures take place at different stages of the pipeline Off-line features are selected from the shape and appearance model before matching, that is, under a prior ensemble state hypothesis sk , assuming that predicted states are close to the current view In most cases, this involves rendering the model into an off-screen image, detecting features, and back-projecting them in object space, eventually using the depth buffer.

Online features are, instead, updated from the current image after state update, that is, under a posterior state sk, because posterior states are matched to the image data, in the ideal case giving the correct backprojection, in the absence of estimation errors Depending on the modality and on the visibility conditions of the shape, these procedures can sometimes be performed once before tracking (eg, for planar objects), whereas in other cases they must be repeated more or less often for different views, in the worst case at every frame In any case, being expensive procedures, they are usually performed for a single ensemble state, usually coincident with the average of the distribution sk (respectively, sk).

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Combining both types of features provides more stability and robustness [157]: in fact, off-line features are stable but cannot adapt to the environmental conditions, such as variable lighting or external occlusions, whereas online features are always up-to-date but may accumulate errors during the backprojection because of state estimation errors Therefore, the former may give jittering pose estimates, while the latter may suffer from drift phenomena However, fusion of off-and online features has a synergic effect, which improves tracking quality; in fact, off-line features prevent drift, in turn allowing safe back-projection of the online features, contributing overall to more precise and robust results To provide a few examples: Foreground color statistics can be collected and updated from the last estimation result, as well as sampled from the appearance model rendered.

A background model for segmentation can also be updated during time and kept along with the initial model, which acts as a constant reference for regions that have erroneously been updated with moving objects The same holds for shape statistics used for foreground blob matching, where the current blob shape can be re ned online, while the offline model may provide a coarse but constant version (eg, a simple rectangular shape for representing a person)..

Scheme 5-30

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