Multiview segmentation and tracking of dynamic occluding layers

We present an algorithm for the layered segmentation of video data in multiple views. The approach is based on computing the parameters of a layered representation of the scene in which each layer is modelled by its motion, appearance and occupancy, where occupancy describes, probabilistically, the layer’s spatial extent and not simply its segmentation in a particular view. The problem is formulated as the MAP estimation of all layer parameters conditioned on those at the previous time step; i.e., a sequential estimation problem that is equivalent to tracking multiple objects in a given number views. Expectation–Maximisation is used to establish layer posterior probabilities for both occupancy and visibility, which are represented distinctly. Evidence from areas in each view which are described poorly under the model is used to propose new layers automatically. Since these potential new layers often occur at the fringes of images, the algorithm is able to segment and track these in a single view until such time as a suitable candidate match is discovered in the other views. The algorithm is shown to be very effective at segmenting and tracking non-rigid objects and can cope with extreme occlusion. We demonstrate an application of this representation to dynamic novel view synthesis.