Region-based tracking using sequences of relevance measures

We present the preliminary results of our proposal: a region-based detection and tracking method of arbitrary shapes. The method is designed to be robust against orientation and scale changes and also occlusions. In this work, we study the effectiveness of sequence of shape descriptors for matching purpose. We detect and track surfaces by matching the sequences of descriptor so called relevance measures with their correspondences in the database. First, we extract stable shapes as the detection target using Maximally Stable Extreme Region (MSER) method. The keypoints on the stable shapes are then extracted by simplifying the outline of the stable regions. The relevance measures that are composed by three keypoints are then computed and the sequences of them are composed as descriptors. During runtime, the sequences of relevance measures are extracted from the captured image and are matched with those in the database. When a particular region is matched with one in the database, the orientation of the region is then estimated and virtual annotations can be superimposed. We apply this approach in an interactive task support system that helps users for creating paper craft objects.