A Probabilistic Contour Observer for Online Visual Tracking

This paper presents an online, recursive filtering strategy for contour-based tracking. Approaching the tracking problem from an estimation perspective leads to an observer design for the visual track signal associated with an individual target in an image sequence. The track state of the observer is decomposed into group and shape components that describe the gross location and the nonrigid shape, respectively, of the object. A probabilistic representation describes the shape nonparametrically. The constitutive components of the observer are detailed, which include a dynamical prediction model and a correction mechanism. Incorporating the probabilistic observer into the tracking process leads to improved performance and segmentations. The improvements are validated through application of the observer to recorded imagery with evaluation via objective measures of quality.

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