Egocentric hand pose estimation and distance recovery in a single RGB image
Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2016
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/80278 http://hdl.handle.net/10220/40401 |
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| 總結: | Articulated hand pose recovery in egocentric vision is useful
for in-air interaction with the wearable devices, such as the
Google glasses. Despite the progress obtained with the depth
camera, this task is still challenging with ordinary RGB cameras.
In this paper we demonstrate the possibility to recover
both the articulated hand pose and its distance from the camera
with a single RGB camera in egocentric view. We address
this problem by modeling the distance as a hidden variable
and use the Conditional Regression Forest to infer the pose
and distance jointly. Especially, we find that the pose estimation
accuracy can be further enhanced by incorporating the
hand part semantics. The experimental results show that the
proposed method achieves good performance on both a synthesized
dataset and several real-world color image sequences
that are captured in different environments. In addition, our
system runs in real-time at more than 10fps. |
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