Visual motion estimation and prediction: a probabilistic network model for temporal coherence

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate motion flows in the image sequences. Our theory is expressed in terms of the Bayesian generalization of standard Kalman filtering which allows us to solve temporal grouping in conjunction with prediction and estimation. As demonstrated for tracking isolated contours the Bayesian formulation is superior to approaches which use data association as a first stage followed by conventional Kalman filtering. Our computer simulations demonstrate that our theory qualitatively accounts for several psychophysical experiments on motion occlusion and motion outliers