Qualitative Multiscale Feature Hierarchies for Object Tracking

Abstract

This paper shows how the performance of feature trackers can be improved by building a hierarchical view-based object representation consisting of qualitative relations between image structures at different scales. The idea is to track all image features individually and to use the qualitative feature relations for avoiding mismatches, for resolving ambiguous matches, and for introducing feature hypotheses whenever image features are lost. Compared to more traditional work on view-based object tracking, this methodology has the ability to handle semirigid objects and partial occlusions. Compared to trackers based on three-dimensional object models, this approach is much simpler and of a more generic nature. A hands-on example is presented showing how an integrated application system can be constructed from conceptually very simple operations.