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Tracking and Interaction Based on Hybrid Sensing for Virtual Environments

  • Received : 2012.04.24
  • Accepted : 2012.12.07
  • Published : 2013.04.01

Abstract

We present a method for tracking and interaction based on hybrid sensing for virtual environments. The proposed method is applied to motion tracking of whole areas, including the user's occlusion space, for a high-precision interaction. For real-time motion tracking surrounding a user, we estimate each joint position in the human body using a combination of a depth sensor and a wand-type physical user interface, which is necessary to convert gyroscope and acceleration values into positional data. Additionally, we construct virtual contents and evaluate the validity of results related to hybrid sensing-based whole-body tracking of human motion methods used to compensate for the occluded areas.

Keywords

References

  1. G.A. Lee et al., "Virtual Reality Content-Based Training for Spray Painting Tasks in the Shipbuilding Industry," ETRI J., vol. 32, no. 5, Oct. 2010, pp. 695-703. https://doi.org/10.4218/etrij.10.1510.0105
  2. D.S. Jo, U.Y. Yang, and W.H. Son, "Design Evaluation System with Visualization and Interaction of Mobile Devices Based on Virtual Reality Prototypes," ETRI J., vol. 30, no. 63, Dec. 2008, pp. 757-764. https://doi.org/10.4218/etrij.08.0108.0209
  3. V. Ganapathi et al., "Real Time Motion Capture Using a Single Time-of-Flight Camera," Proc. ICCV, 2010, pp. 755-762.
  4. C. Wren et al., "Pfinder: Real-Time Tracking of the Human Body," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, July 1997, pp. 780-785. https://doi.org/10.1109/34.598236
  5. Y. Liu et al., "Markerless Motion Capture of Interacting Characters Using Multi-view Image Segmentation," Proc. CVPR, 2011, pp. 1249-1256.
  6. C. Plagemann et al., "Real-Time Identification and Localization of Body Parts from Depth Images," Proc. ICRA, 2010, pp. 3108-3113.
  7. J. Shotton et al., "Real-Time Human Pose Recognition in Parts from Single Depth Images," Proc. CVPR, 2011, pp. 1297-1304.
  8. K. Khoshelham, "Accuracy Analysis of Kinect Depth Data," Proc. ISPRS, 2011, pp. 29-31.
  9. S.H. Kwak et al., "Learning Occlusion with Likelihoods for Visual Tracking," Proc. ICCV, 2011, pp. 1551-1558.
  10. D.H. Kim and D.J. Kim, "Self-Occlusion Handling for Human Body Motion Tracking from 3D ToF Image Sequence," Proc. ACMMM 3DVP, 2010, pp. 57-62.
  11. Y.-L. Chou, Statistical Analysis: With Business and Economic Applications, 2nd ed., New York: Holt, Rinehart & Winston of Canada Ltd, 1975, Section 17.9.
  12. S.R. Buss and J.S. Kim, "Selectively Damped Least Squares for Inverse Kinematics," J. Graphics Tool, vol. 10, no. 3, 2005, pp. 37-49.

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