N-tier Simultaneous Modelling and Tracking for Arbitrary Warps

Abstract

This paper presents an approach to object tracking which, given a single example of a target, learns a hierarchical constellation model of appearance and structure on the fly. The model becomes more robust over time as evidence of the variability of the object is acquired and added to the model. Tracking is performed in an optimised Lucas-Kanade type framework, using Mutual Information as a similarity metric. Several novelties are presented: an improved template update strategy using Bayes theorem, a multi-tier model topology, and a semi-automatic testing method. A critical comparison with other methods is made using exhaustive testing. In all 11 challenging test sequences were used with a mean length of 568 frames.

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