Learning to Track the Visual Motion of Contours

Abstract A development of a method for tracking visual contours is described. Given an “untrained” tracker, a training motion of an object can be observed over some extended time and stored as an image sequence. The image sequence is used to learn parameters in a stochastic differential equation model. These are used, in turn, to build a tracker whose predictor imitates the motion in the training set. Tests show that the resulting trackers can be markedly tuned to desired curve shapes and classes of motions.

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