Abstract
This paper presents the implementation of real-time tracking algorithm for following and evaluating the 3D position of a generic spatial object. The key issue of our approach is the development of a new algorithm for pattern recognition in machine vision, the Least Constrained Square-Fitting of Ellipses (LCSE), which improves the state of the art ellipse fitting procedures. It is a robust and direct method for the least-square fitting of ellipses to scattered data. Although it has been ellipse-specifically developed, our algorithm demonstrates to be well suitable for the real-time tracking any spherical object, and it presents also robustness against noise. In this work we applied it to the RobotCub humanoid robotics platform simulator. We compared its performance with the Hough Transform, in terms of robustness (success/failure in the object detection) and fitting precision. We performed several tests to prove the robustness of the algorithm within the overall system. Finally we present our results.
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