| Abstract |
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A method is presented to estimate 3D head poses
effectively by hybrid sensing of depth and gray
information. Depth information is used to generate clean
head segmentation even in a cluttered scene. Based on
the segmentation result, sparse optical flow at head
region is extracted and used for 3D head motion
estimation in video rate. This method is shown to be
effective through experiments on video sequences. Our
method provides an alternative way for 3D head pose
estimation from an image sequence in the current
computer vision literature. Moreover, the depth
information is incorporated into the estimation step as
regularization for noisy motion estimation problem.
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 Additional Information
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Citation:
Youding Zhu, Kikuo Fujimura,
"3D Head Pose Estimation with Optical Flow and Depth Constraints,"
3dim,
p. 211,
Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM '03),
2003
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