Hand Orientation Regression Using Random Forest for Augmented Reality

Conference paper

DOI: 10.1007/978-3-319-13969-2_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8853)
Cite this paper as:
Asad M., Slabaugh G. (2014) Hand Orientation Regression Using Random Forest for Augmented Reality. In: De Paolis L., Mongelli A. (eds) Augmented and Virtual Reality. AVR 2014. Lecture Notes in Computer Science, vol 8853. Springer, Cham


We present a regression method for the estimation of hand orientation using an uncalibrated camera. For training the system, we use a depth camera to capture a large dataset of hand color images and orientation angles. Each color image is segmented producing a silhouette image from which contour distance features are extracted. The orientation angles are captured by robustly fitting a plane to the depth image of the hand, providing a surface normal encoding the hand orientation in 3D space. We then train multiple Random Forest regressors to learn the non-linear mapping from the space of silhouette images to orientation angles. For online testing of the system, we only require a standard 2D image to infer the 3D hand orientation. Experimental results show the approach is computationally efficient, does not require any camera calibration, and is robust to inter-person shape variation.


Orientation estimation Random forest regression Silhouette image Hand 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.City University LondonLondonUK

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