Author :
Mansouri, Abdol-Reza ; Mitiche, Amar ; Aron, Michael
Author_Institution :
Div. of Eng. & Appl. Mathematics, Harvard Univ., Cambridge, MA, USA
Abstract :
In this paper, we propose a novel algorithm for region tracking without motion computation that uses as its starting point the Bayesian framework for tracking previously developed [A.-R. Mansouri, July 2002], [A.R. Mansouri, A. Mitiche, 2002], extending it to a multi-frame tracking algorithm in which tracking is expressed as segmentation in the spatio-temporal domain [A.Mitiche, et al., April 2002]. Our proposed algorithm is expressed as the solution of level set partial differential equations and the tracked region in a particular frame of the sequence is then obtained as the time slice of the level surface given by the level set equations. The main benefit of our proposed algorithm is that contrary to numerous other tracking algorithms, it is a multi-frame tracking algorithm which does not assume the motion to be small [M. Bertalmio, et al., 1998], nor the background to be stationary [S. Jehan-Besson, et al., 2000], nor the region to be uniform in intensity on a uniform background [N. Paragios, R. Deriche, 2000], nor does it assume any motion models [A. Mitiche, et al., April 2002]. This leads to a tracking algorithm which combines the advantages of the Bayesian formulation developed for frame-to-frame tracking [A.-R. Mansouri, July 2002] with those of the formulation in the spatio-temporal domain [A. Mitiche, et al., April 2002]. We illustrate the performance of our algorithm on real image sequences with natural motion.
Keywords :
Bayes methods; computer vision; image segmentation; image sequences; partial differential equations; spatiotemporal phenomena; Bayesian framework; PDE-based region tracking; computer vision; frame-to-frame tracking; image processing; image sequence; joint space-time segmentation; motion computation; multiframe tracking algorithm; partial differential equation; spatio-temporal domain; Application software; Bayesian methods; Computer vision; Image processing; Image segmentation; Image sequences; Level set; Mathematics; Partial differential equations; Tracking;