Visual Feature Tracking with Automatic Motion Model Switching

In this paper we present a motion determination and tracking technique based on the combination of Bayesian multiple hypothesis and a Multiple Model Filtering (MMF) algorithm. Corner features appearing in the initial frame of an image sequence were predicted in the subsequent frames using an extension of the multiple hypothesis algorithm (MHT [I]) based on different motion models. The collection of data provided by such a system was then provided to a Mh1F algorithm to determine the correct motion of features. We considered drferent order velociry and acceleration modelsfor the MMF algorithm and applied them to two image sequences, the PUMA and Toy car sequences. The study shows that the method proposed can distinguish between drfferent motions depicted in an image sequence with very good tracking results. 1