Toward augmenting everything: Detecting and tracking geometrical features on planar objects

Abstract

This paper presents an approach for detecting and tracking various types of planar objects with geometrical features. We combine traditional keypoint detectors with Locally Likely Arrangement Hashing (LLAH) [21] for geometrical feature based keypoint matching. Because the stability of keypoint extraction affects the accuracy of the keypoint matching, we set the criteria of keypoint selection on keypoint response and the distance between keypoints. In order to produce robustness to scale changes, we build a non-uniform image pyramid according to keypoint distribution at each scale. In the experiments, we evaluate the applicability of traditional keypoint detectors with LLAH for the detection. We also compare our approach with SURF and finally demonstrate that it is possible to detect and track different types of textures including colorful pictures, binary fiducial markers and handwritings.