We describe a probabilistic framework for detecting and tracking motion boundaries. It builds on previous work [4] that used a particle filter to compute a posterior distribution over multiple, local motion models, one of which was specific for motion boundaries. We extend that framework in two ways: 1) with an enhanced likelihood that combines motion and edge support, 2) with a spatiotemporal model that propagates beliefs between adjoining image neighborhoods to encourage boundary continuity and provide better temporal predictions for motion boundaries. Approximate inference is achieved with a combination of tools: Sampled representations allow us to represent multimodal non-Gaussian distributions and to apply nonlinear dynamics. Mixture models are used to simplify the computation of joint prediction distributions.