Abstract
In this paper, we propose a practical hand skeleton reconstruction method using a monocular camera. The proposed method is a fundamental technology that can be applicable to future products such as wearable or mobile devices and smart TVs requiring natural hand interactions. To heighten its practicability, we designed our own hand parameters composed of global hand and local finger configurations. Based on the parameter states, a kinematic hand and its contour can be reconstructed. By adopting palm detection and tracking, global parameters can be easily estimated, which can reduce the search space required for whole parameter estimations. We can then fine-tune the coarse estimated parameters through the use of a Gauss-Newton optimization stage. Experimental results indicate that our method provides a sufficient level of accuracy to be utilized in gesture-interactive applications. The proposed method is light in terms of algorithm complexity and can be applied in real time.
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References
Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., Twombly, X.: Vision-based hand pose estimation: a review. Comput. Vis. Image Underst. 108(1), 52–73 (2007)
Cheng, H., Yang, L., Liu, Z.: A survey on 3d hand gesture recognition. In: IEEE Transactions on Circuits and Systems for Video Technology (to appear)
Rehg, J.M., Kanade, T.: Digiteyes: vision-based hand tracking for human-computer interaction. In: Proceedings of the 1994 IEEE Workshop, pp. 16–22 (1994)
de La Gorce, M., Fleet, D.J., Paragios, N.: Model-based 3D hand pose estimation from monocular video. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1793–1805 (2011)
Keskin, C., Kirac, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: IEEE ICCV Workshops (2011)
Keskin, C., Kirac, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer Depth Cameras for Computer Vision: Research Topics and Applications. Advances in Computer Vision and Pattern Recognition, pp. 119–137. Springer, London (2013). doi:10.1007/978-1-4471-4640-7_7
Lee, J., Kunii, T.L.: Constraint-based hand animation. In: Thalmann, N.M., Thalmann, D. (eds.) Models and Techniques in Computer Animation. Computer Animation Series, pp. 110–127. Springer, Tokyo (1993). doi:10.1007/978-4-431-66911-1_11
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. stat. 28(2), 337–407 (2000)
Kublbeck, C., Ernst, A.: Face detection and tracking in video sequences using the modifiedcensus transformation. Image Vis. Comput. 24(6), 564–572 (2006)
Bae, S., Hong, S., Choi, Y., Yang, H.S.: Recursive Bayesian fire recognition using greedy margin-maximizing clustering. Mach. Vis. Appl. 24(8), 1605–1621 (2013)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15 (1988)
Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report CMU-CS-91-132 (1991)
Dennis Jr., J.E., Schnabel, R.B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Upper Saddle River (1996)
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Bae, S., Yoo, J., Jeong, M., Savin, V. (2016). Practical Hand Skeleton Estimation Method Based on Monocular Camera. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_38
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DOI: https://doi.org/10.1007/978-3-319-50832-0_38
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