1 April 2004 Projection model snakes for tracking using a Monte Carlo approach
Author Affiliations +
J. of Electronic Imaging, 13(2), (2004). doi:10.1117/1.1687732
We propose a method that uses projection models in conjunction with a sequential Monte Carlo approach to track rigid targets. We specifically address the problems associated with tracking objects in scenarios characterized by cluttered images and high variability in target scale. The projection model snake is introduced in order to track a target boundary over a variety of scales by geometrically transforming the boundary to account for three-dimensional relative motion between the target and camera. The complete solution is a potent synergism of the projection model snake and a sequential Monte Carlo method. The projection model Monte Carlo method randomly generates the parameters of target motion and pose from empirically derived distributions. The resultant "particles" are then weighted according to a likelihood determined by the integration of the mean gradient magnitude around the target contour, yielding the expected target path and pose. We demonstrate the effectiveness of this approach for tracking dynamic targets in sequences with noise, clutter, occlusion, and scale variability.
Michael E. Welser, Scott Thomas Acton, "Projection model snakes for tracking using a Monte Carlo approach," Journal of Electronic Imaging 13(2), (1 April 2004). http://dx.doi.org/10.1117/1.1687732

Affine motion model

Monte Carlo methods

Detection and tracking algorithms


Particle filters

Solid modeling

3D modeling

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