Abstract
In this paper, a fast head pose tracking system is introduced. It uses iterative closest point algorithm to register a dense face template to depth data captured by Kinect. It can achieve 33fps processing speed without specific optimization. To improve tracking robustness, head movement prediction is applied. We propose a novel scheme that can train several simple predictors together, enhancing the overall prediction accuracy. Experimental results confirm its effectiveness for head movement prediction.
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Li, S., Ngan, K.N., Sheng, L. (2013). A Head Pose Tracking System Using RGB-D Camera. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_16
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DOI: https://doi.org/10.1007/978-3-642-39402-7_16
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