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Hand Orientation Regression Using Random Forest for Augmented Reality

Asad, M. & Slabaugh, G. G. (2014). Hand Orientation Regression Using Random Forest for Augmented Reality. In: DePaolis, L.T. & Mongelli, A. (Eds.), Augmented and Virtual Reality. AVR 2014. Lecture Notes in Computer Science, 8853. (pp. 159-174). Berlin, Germany: Springer Verlag. doi: 10.1007/978-3-319-13969-2_13


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.

Publication Type: Book Section
Additional Information: The final publication is available at Springer via
Publisher Keywords: orientation estimation, Random Forest regression, silhouette image, hand
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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