Edge-Based Template Matching and Tracking for Perspectively Distorted Planar Objects

This paper presents a template matching approach to high accuracy detection and tracking of perspectively distorted objects. To this end we propose a robust match metric that allows significant perspective shape changes. Using a coarse-to-fine representation for the detection of the template further increases efficiency. Once a template is detected at interactive frame-rate, we immediately switch to tracking with the same algorithm, enabling detection times of only 20ms. We show in a number of experiments that the presented approach is not only fast, but also very robust and highly accurate in detecting the 3D pose of planar objects or planar subparts of non-planar objects. The approach is used in augmented reality applications that could up to now not be sufficiently solved because existing approaches either needed extensive training data, like machine learning methods, or relied on interest point extraction, like descriptors-based methods.