Adaptive Vision for Playing Table Soccer

For real time object recognition and tracking often color-based methods are used. While these methods are very efficient, they usually dependent heavily on lighting conditions. In this paper we present a robust and efficient vision system for the table soccer robot KiRo. By exploiting knowledge about invariant characteristics of the table soccer game, the system is able to adapt to changing lighting conditions dynamically and to detect relevant objects on the table within a few milliseconds. We give experimental evidence for the robustness and efficiency of our approach.