Explaining Optical Flow Events with Parameterized Spatio-Temporal Models

A spatio-temporal representation for complex optical flow events is developed that generalizes traditional parameterized motion models (e.g. afine). These generative spatio-temporal models may be non-linear or stochastic and are event-specijic in that they characterize a particular type of object motion (e.g. sitting or walking). Within a Bayesian framework we seek the appropriate model, phase, rate, spatial poFigure 1: Challenges for motion estimationlexplanation. sition, and scale to account for the image variation. The posterior distribution over this parameter space conditioned on image measurements is typically nonGaussian. The distribution is represented using factored sampling and is predicted and updated over time using the Condensation algorithm. The resulting framework automatically detects, localizes, and recognizes motion events.