We describe a probabilistic framework for detecting and tracking motion boundaries. It builds on previous work (M.J. Black and D.J. Fleet, 2000) 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, while mixture models are used to simplify the computation of joint prediction distributions.
Probabilistic tracking of motion boundaries with spatiotemporal predictions
2001-01-01
1773055 byte
Conference paper
Electronic Resource
English
Probabilistic Tracking of Motion Boundaries with Spatiotemporal Predictions
British Library Conference Proceedings | 2001
|Probabilistic Detection and Tracking of Motion Boundaries
British Library Online Contents | 2000
|Probabilistic Tracking and Recognition of Non-Rigid Hand Motion
British Library Conference Proceedings | 2003
|