PWP3D: Real-time segmentation and tracking of 3d objects

We formulate a probabilistic framework for simultaneous 2D segmentation and 2D– 3D pose tracking, using a known 3D model (of arbitrary shape) of the segmented object. Our technique is region-based; at each frame we maximise the discrimination between statistical foreground and background models, by adjusting the pose parameters itera-tively. Unlike all previous work in 3D tracking, we use posterior membership proba-bilities for foreground and background pixels, rather than pixel likelihoods, and during periods of stable tracking we allow adaptation of the statistical foreground and back-ground models. We support our ideas with a real-time implementation, and use this to generate experimental results on both real and artificial video sequences, with a number of 3D models, to showcase the qualities of our tracker, and to demonstrate the benefit of using pixel-wise posteriors rather than likelihoods.