Abstract
Research in vision-based 3D hand tracking targets primarily the scenario in which a bare hand performs unconstrained motion in front of a camera system. Nevertheless, in several important application domains, augmenting the hand with color information so as to facilitate the tracking process constitutes an acceptable alternative. With this observation in mind, in this work we propose a modification of a state of the art method [12] for markerless 3D hand tracking, that takes advantage of the richer observations resulting from a colored glove. We do so by modifying the 3D hand model employed in the aforementioned hypothesize-and-test method as well as the objective function that is minimized in its optimization step. Quantitative and qualitative results obtained from a comparative evaluation of the baseline method to the proposed approach confirm that the latter achieves a remarkable increase in tracking accuracy and robustness and, at the same time, reduces drastically the associated computational costs.
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The optimization space has one more dimension than the degrees of freedom of the hand model due to the quaternion representation of 3D hand orientation.
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References
Athitsos, V., Sclaroff, S.: Estimating 3D hand pose from a cluttered image. In: CVPR, pp. II-432–II-439. IEEE (2003)
Ballan, L., Taneja, A., Gall, J., Van Gool, L., Pollefeys, M.: Motion capture of hands in action using discriminative salient points. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 640–653. Springer, Heidelberg (2012)
de La Gorce, M., Fleet, D.J., Paragios, N.: Model-based 3D hand pose estimation from monocular video. IEEE Trans. PAMI 33, 1–15 (2011)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 39(1), 1–38 (1977)
Dorner, B.: Chasing the colour glove: visual hand tracking. Ph.D. thesis, Simon Fraser University (1994)
Hamer, H., Schindler, K., Koller-Meier, E., Van Gool, L.: Tracking a hand manipulating an object. In: ICCV, pp. 1475–1482 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE, January 1995
Kennedy, J., Eberhart, R., Yuhui, S.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Keskin, C., Kirac, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: ICCV Workshop, pp. 1228–1234 (2011)
Oikonomidis, I., Argyros, A.A.: Evolutionary quasi-random search for hand articulations tracking. In: IEEE CVPR, Columbus, Ohio, USA (2014)
Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Markerless and efficient 26-DOF hand pose recovery. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 744–757. Springer, Heidelberg (2011)
Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient model-based 3d tracking of hand articulations using kinect. In: BMVC, Dundee, UK (2011)
Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Tracking the articulated motion of two strongly interacting hands. In: CVPR, pp. 1862–1869. IEEE, June 2012
Rehg, J.M., Kanade, T.: Model-based tracking of self-occluding articulated objects. In: ICCV, p. 612, Los Alamitos, CA, USA. IEEE Computer Society (1995)
Romero, J., Kjellstrom, H., Kragic, D.: Monocular real-time 3D articulated hand pose estimation. In: IEEE-RAS International Conference on Humanoid Robots. IEEE, December 2009
Rosales, R., Athitsos, V., Sigal, L., Sclaroff, S.: 3D hand pose reconstruction using specialized mappings. In: ICCV (2001)
Schroder, M., Elbrechter, C., Maycock, J., Haschke, R., Botsch, M., Ritter, H.: Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands. In: 2012 12th IEEE-RAS International Conference on Humanoid Robots, pp. 262–269. IEEE (2012)
Sudderth, E.B., Mandel, M.I., Freeman, W.T., Willsky, A.S.: Visual hand tracking using nonparametric belief propagation. In: CVPR Workshop on Generative Model-based Vision (2004)
Theobalt, C., Albrecht, I., Haber, J., Magnor, M., Seidel, H.-P.: Pitching a baseball - tracking high-speed motion with multi-exposure images. In: Proceedings of the SIGGRAPH 2004, pp. 540–547. ACM SIGGRAPH (2004)
Wang, R.Y., Popović, J.: Real-time hand-tracking with a color glove. ACM Trans. Graph. 28(3), 63:1–63:8 (2009)
Wu, Y., Huang, T.S.: View-independent recognition of hand postures. In: CVPR, vol. 2, pp. 88–94. IEEE (2000)
Acknowledgments
This work was partially supported by the EU FP7-ICT-2011-9 project WEARHAP.
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Roditakis, K., Argyros, A.A. (2015). Quantifying the Effect of a Colored Glove in the 3D Tracking of a Human Hand. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_36
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