Abstract
This paper presents a fast but robust method to recognize articulated hand pose from single depth images in real-time. We tackle the main challenges in the hand pose recognition, which include the high degree of freedom and self-occlusion of articulated hand motion, using efficient retrieval of a large set of hand pose templates. The normalized orientation templates are used for encoding the depth images containing hand poses, and the locality sensitive hashing is used for finding the nearest neighbors in real time. Our approach does not suffer from the common problems in the conventional tracking approaches such as model initialization and tracking drift, and qualitatively outperforms the existing hand pose estimation techniques.
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References
Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., Twombly, X.: Vision-based hand pose estimation: A review. Computer Vision and Image Understanding 108(1), 52–73 (2007)
Wang, R.Y., Popović, J.: Real-time hand-tracking with a color glove. ACM Transactions on Graphics (TOG) 28, 63 (2009)
Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient model-based 3d tracking of hand articulations using kinect. In: BMVC, pp. 1–11 (2011)
Melax, S., Keselman, L., Orsten, S.: Dynamics based 3d skeletal hand tracking. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D 2013, p. 184. ACM, New York (2013)
Tang, D., Yu, T.-H., Kim, T.-K.: Real-time articulated hand pose estimation using semi-supervised transductive regression forests. In: The IEEE International Conference on Computer Vision (ICCV) (December 2013)
Xu, C., Cheng, L.: Efficient hand pose estimation from a single depth image. In: The IEEE International Conference on Computer Vision (ICCV) (December 2013)
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(12), 2821–2840 (2013)
Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.: Gradient response maps for real-time detection of textureless objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(5), 876–888 (2012)
Baran, I., Popović, J.: Automatic rigging and animation of 3d characters. In: ACM SIGGRAPH 2007 Papers. ACM, New York (2007)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: VLDB, vol. 99, pp. 518–529 (1999)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: VISAPP (1), pp. 331–340 (2009)
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Cha, YW., Lim, H., Sung, MH., Ahn, S.C. (2014). Depth Data-Driven Real-Time Articulated Hand Pose Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_47
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DOI: https://doi.org/10.1007/978-3-319-14364-4_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
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