Benchmarking Binarisation techniques for 2D fiducial marker tracking

Abstract

This paper proposes a comparative study of different binarisation techniques for 2D fiducial marker tracking. The application domain is the recognition of objects for Tangible User Interface (TUI) using a tabletop solution. In this case, the common technique is to use markers, attached to the objects, which can be identified using camera-based pattern recognition techniques. Among the different operations that lead to a good recognition of these markers, the step of binarisation of greyscale image is the most critical one. We propose to investigate how this important step can be improved not only in terms of quality but also in term of computational efficiency. State-of-the-art thresholding techniques are benchmarked on this challenging task. A real-world tabletop TUI is used to perform an objective and goal oriented evaluation through the ReacTIVision framework. A computational efficient implementation of one of the best window-based thresholders is proposed in order to satisfy the real-time processing of a video stream. The experimental results reveal that an improvement of up to 10 points of the fiducial tracking recognition rate can be reached when selecting the right thresholder over the embedded method while being more robust and still remaining time-efficient. Copyright © 2014 SCITEPRESS.