Abstract
The biggest challenge in hand detection and tracking is the high dimensionality of the hand’s kinematic configuration space of about 30 degrees of freedom, which leads to a huge variance in its projections. This makes it difficult to come to a tractable model of the hand as a whole. To overcome this problem, we suggest to concentrate on posture invariant local constraints, that exist on finger appearances. We show that, besides skin color, there is a number of additional geometric and photometric invariants. This paper presents a novel approach to real-time hand detection and tracking by selecting local regions that comply with these posture invariants. While most existing methods for hand tracking rely on a color based segmentation as a first preprocessing step, we integrate color cues at the end of our processing chain in a robust manner. We show experimentally that our approach still performs robustly above cluttered background, when using extremely low quality skin color information. With this we can avoid a user- and lighting-specific calibration of skin color before tracking.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kolsch, M., Turk, M.: Robust hand detection. In: Int. Conf. on Automatic Face and Gesture Recognition, May 17–19, pp. 614–619 (2004)
Athitsos, V., Sclaroff, S.: Estimating 3d hand pose from a cluttered image. In: CVPR, June 2003, vol. 2, pp. II–432–II–439 (2003)
de La Gorce, M., Paragios, N., Fleet, D.: Model-based hand tracking with texture, shading and self-occlusions. In: CVPR (2008)
Rehg, J., Kanade, T.: Digiteyes: vision-based hand tracking for human-computer interaction. In: Workshop on Motion of Non-Rigid and Articulated Objects, November 1994, pp. 16–22 (1994)
Stenger, B., Thayananthan, A., Torr, P., Cipolla, R.: Model-based hand tracking using a hierarchical bayesian filter. PAMI 28(9), 1372 (2006)
von Hardenberg, C., Bérard, F.: Bare-hand human-computer interaction. In: Workshop on Perceptive User Interfaces, pp. 1–8. ACM, New York (2001)
Lee, T., Hollerer, T.: Handy ar: Markerless inspection of augmented reality objects using fingertip tracking. In: Int. Symp. on Wearable Computers, pp. 83–90 (2007)
Oka, K., Sato, Y., Koike, H.: Real-time fingertip tracking and gesture recognition. IEEE Computer Graphics and Applications 22(6), 64–71 (2002)
Maccormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Koller, T., Gerig, G., Szekely, G., Dettwiler, D.: Multiscale detection of curvilinear structures in 2-d and 3-d imagedata. In: ICCV, pp. 864–869 (1995)
Steger, C.: Extracting curvilinear structures: A differential geometric approach. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 630–641. Springer, Heidelberg (1996)
Bradski, G.R., et al.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal 2(2), 12–21 (1998)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. PAMI 24(5), 603–619 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Petersen, N., Stricker, D. (2009). Fast Hand Detection Using Posture Invariant Constraints. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-04617-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04616-2
Online ISBN: 978-3-642-04617-9
eBook Packages: Computer ScienceComputer Science (R0)