Abstract. We present an improved framework for real-time segmentation and tracking by fusing depth and RGB color data. We are able to solve common problems seen in tracking and segmentation of RGB images, such as occlusions, fast motion, and objects of similar color. Our proposed real-time mean shift based algorithm outperforms the current state of the art and is significantly better in difficult scenarios.

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Abstract-We present a new solution for real-time head pose estimation. The key to our method is a model-based approach based on the fusion of color and time-of-flight depth data. Our method has several advantages over existing head-pose estimation solutions. It requires no initial setup or knowledge of a pre-built model or training data. The use of additional depth data leads to a robust solution, while maintaining real-time performance. The method outperforms the state-of-the art in several experiments using extreme situations such as sudden changes in lighting, large rotations, and fast motion.

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