Tracking Objects Using Density Matching and Shape Priors

We present a novel method for tracking objects by combining density matching with shape priors. Density matching is a tracking method which operates by maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Such trackers can be expressed as PDE-based curve evolutions, which can be implemented using level sets. Shape priors can be combined with this levelset implementation of density matching by representing the shape priors as a series of level sets; a variational approach allows for a natural, parametrization-independent shape term to be derived. Experimental results on real image sequences are shown.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Tao Zhang, Daniel Freedman
• Keywords: Bhattacharyya Similarity Measure, Computer Vision, Density Matching, ICCV 2003, Model Photometric Distribution, Parametrization-independent Shape Term, Shape Priors