Wearable Device for High-Speed Hand Pose Estimation with a Ultrasmall Camera

Motomasa Tomida; Kiyoshi Hoshino

doi:10.20965/jrm.2015.p0167

single-rb.php

« previous

JRM Vol.27 No.2 pp. 167-173

doi: 10.20965/jrm.2015.p0167

(2015)

Paper:

Views over last 60 days: 1,320

Wearable Device for High-Speed Hand Pose Estimation with a Ultrasmall Camera

Motomasa Tomida and Kiyoshi Hoshino

Graduate School of Systems and Information Engineering, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibakaki 305-0006, Japan

Received:

September 10, 2014

Accepted:

January 14, 2015

Published:

April 20, 2015

Keywords:

wearable hand capture, hand pose estimation, dimension compression of image characteristic quantity, hand image without fingertip, remote robot control

Abstract

Hand pose estimation with ultrasmall camera

Operating a robot intentionally by using various complex motions of the hands and fingers requires a system that accurately detects hand and finger motions at high speed. This study uses an ultrasmall camera and compact computer for development of a wearable device of hand pose estimation, also called a hand-capture device. The accurate estimations, however, require data matching with a large database. But a compact computer usually has only limited memory and low machine power. We avoided this problem by reducing frequently used image characteristics from 1,600 dimensions to 64 dimensions of characteristic quantities. This saved on memory and lowered computational cost while achieving high accuracy and speed. To enable an operator to wear the device comfortably, the camera was placed as close to the back of the hand as possible to enable hand pose estimation from hand images without fingertips. A prototype device with a compact computer used to evaluate performance indicated that the device achieved high-speed estimation. Estimation accuracy was 2.32°±14.61° at the PIP joint of the index finger and 3.06°±10.56° at the CM joint of the thumb – as accurate as obtained using previous methods. This indicated that dimensional compression of image-characteristic quantities is important for realizing a compact hand-capture device.

Cite this article as:

M. Tomida and K. Hoshino, “Wearable Device for High-Speed Hand Pose Estimation with a Ultrasmall Camera,” J. Robot. Mechatron., Vol.27 No.2, pp. 167-173, 2015.

Data files:

References

[1] N. S. Pollard, J. K. Hodgins, M. J. Riley, and C. G. Atkeson, “Adapting human motion for the control of a humanoid robot,” Proc. of IEEE Int. Conf. on Robotics and Automation, Vol.2, pp. 1390-1397, 2002,
[2] T. Asfour and R. Dillmann, “Human-like motion of a humanoid robot arm based on a closed form solution of the inverse kinematics problem,” Proc. IEEE/RSJ Conf. Intelligent Robots and Systems, pp. 1407-1412, Las Vegas, Nevada, October 2003.
[3] J. M. Rehg and T. Kanade, “Visual tracking of high DOF articulated structures: an application to human hand tracking,” European Conf. Computer Vision, pp. 35-46, 1994.
[4] Y. Kameda and M. Minoh, “A human motion estimation method using 3-successive video frames,” Proc. Virtual Systems and Multimedia, pp. 135-140, 1996.
[5] S. Lu, D. Metaxas, D. Samaras, and J. Oliensis, “Using multiple cues for hand tracking and model refinement,” Proc. CVPR2003, Vol.2, pp. 443-450, 2003.
[6] T. Gumpp, P. Azad, K. Welke, E. Oztop, R. Dillmann, and G. Cheng, “Unconstrained real-time markerless hand tracking for humanoid interaction,” Proc. IEEE-RAS Int. Conf. on Humanoid Robots, CD-ROM, 2006.
[7] V. Athitos and S. Scarloff, “An appearance-based framework for 3D hand shape classification and camera viewpoint estimation,” Proc. Automatic Face and Gesture Recognition, pp. 40-45, 2002.
[8] K. Hoshino and T. Tanimoto, “Real time search for similar hand images from database for robotic hand control,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E88-A, pp. 2514-2520, 2005.
[9] Y. Wu, J. Lin, and T. S. Huang, “Analyzing and capturing articulated hand motion in image sequences,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, pp. 1910-1922, 2005.
[10] K. Hoshino, E. Tamaki, and T. Tanimoto, “Copycat hand – Robot hand imitating human motions at high speed and with high accuracy,” Advanced Robotics, Vol.21, No.15, pp. 1743-1761, 2007.
[11] K. Hoshino and M. Tomida, “3D hand pose estimation using a single camera for unspecified users,” J. of Robotics and Mechatronics, Vol.21, No.6, pp. 749-757, 2009.
[12] N. Otsu and T. Kurita, “A new scheme for practical, flexible and intelligent vision systems,” Proc. IAPR. Workshop on Computer Vision, pp. 431-435, 1998.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] N. S. Pollard, J. K. Hodgins, M. J. Riley, and C. G. Atkeson, “Adapting human motion for the control of a humanoid robot,” Proc. of IEEE Int. Conf. on Robotics and Automation, Vol.2, pp. 1390-1397, 2002,

[2] [2] T. Asfour and R. Dillmann, “Human-like motion of a humanoid robot arm based on a closed form solution of the inverse kinematics problem,” Proc. IEEE/RSJ Conf. Intelligent Robots and Systems, pp. 1407-1412, Las Vegas, Nevada, October 2003.

[3] [3] J. M. Rehg and T. Kanade, “Visual tracking of high DOF articulated structures: an application to human hand tracking,” European Conf. Computer Vision, pp. 35-46, 1994.

[4] [4] Y. Kameda and M. Minoh, “A human motion estimation method using 3-successive video frames,” Proc. Virtual Systems and Multimedia, pp. 135-140, 1996.

[5] [5] S. Lu, D. Metaxas, D. Samaras, and J. Oliensis, “Using multiple cues for hand tracking and model refinement,” Proc. CVPR2003, Vol.2, pp. 443-450, 2003.

[6] [6] T. Gumpp, P. Azad, K. Welke, E. Oztop, R. Dillmann, and G. Cheng, “Unconstrained real-time markerless hand tracking for humanoid interaction,” Proc. IEEE-RAS Int. Conf. on Humanoid Robots, CD-ROM, 2006.

[7] [7] V. Athitos and S. Scarloff, “An appearance-based framework for 3D hand shape classification and camera viewpoint estimation,” Proc. Automatic Face and Gesture Recognition, pp. 40-45, 2002.

[8] [8] K. Hoshino and T. Tanimoto, “Real time search for similar hand images from database for robotic hand control,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E88-A, pp. 2514-2520, 2005.

[9] [9] Y. Wu, J. Lin, and T. S. Huang, “Analyzing and capturing articulated hand motion in image sequences,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, pp. 1910-1922, 2005.

[10] [10] K. Hoshino, E. Tamaki, and T. Tanimoto, “Copycat hand – Robot hand imitating human motions at high speed and with high accuracy,” Advanced Robotics, Vol.21, No.15, pp. 1743-1761, 2007.

[11] [11] K. Hoshino and M. Tomida, “3D hand pose estimation using a single camera for unspecified users,” J. of Robotics and Mechatronics, Vol.21, No.6, pp. 749-757, 2009.

[12] [12] N. Otsu and T. Kurita, “A new scheme for practical, flexible and intelligent vision systems,” Proc. IAPR. Workshop on Computer Vision, pp. 431-435, 1998.