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Chess recognition using 3D patterned illumination camera

Computer Vision has been applied to augment traditional board games such as Chess for a number of reasons. While augmented reality enhances the gaming experience, the required additional hardware (e.g. head gear) is still not widely accepted in everyday leisure activities, and therefore, camera based methods have been developed to interface the computer with the real-life chess board. However, traditional 2D camera approaches suffer from ill-defined environmental conditions (lighting, viewing angle) and are therefore severely limited in their application. To answer this issue, we have incorporated a consumer-grade depth camera based on patterned illumination. We could show that in combination with traditional 2D color images, the recognition of chess pieces is made easier, which allows seamless integration of the real-life chess pieces with the computer program. Our method uses a fusion approach from depth and RGB camera data and is suitable for two distant players to play against each other, using two physical sets of chess.