Visual tracking with automatic motion model switching

This paper provides a novel technique of efficiently and reliably tracking features in a sequence of images. The method we provide for tracking featur es is based on the Bayesian multiple hypothesis tracking (MHT) technique co upled with a multiple model filtering (MMF) algorithm. We show the results of our work comparing it with some of the existing single-model-based track ers using a variety of video sequences. Initially, we demonstrate the abili ty of the MHT-MMF tracker, and later in the paper we extend the MMF-based t racker to the interacting multiple model(IMM) tracker and show the superior ity of the latter in handling motion transition of features efficiently. Th e primary purpose of this paper is to show how the IMM algorithm combined w ith an extension of the classical MHT framework can be used in a visual tra cking scenario. The study shows that the method proposed can distinguish be tween different motions depicted in an image sequence with good tracking re sults. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.