Abstract
In this paper we present a method dedicated to hand tracking in sign language scenes using particle filtering. A a new penalisation method based on the optical flow mechanism is introduced. Generally, particle filters require the use of a reference model. In this paper we have introduced a new method based on a dictionary of visual references of hand to constitute the reference model. The evaluation of our method is performed on the SignStream-ASLLRP database on which we have provided ground truth annotations for this purpose. The obtained results show the accuracy of our method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bhandarkar, S.M., Luo, X.: Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching. CVIU 113(6), 708–725 (2009)
Bradski, G.R.: Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal Q2 (1998)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE Proceedings F Radar and Signal Processing 140(2), 107–113 (1993)
Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. Int. J. Comput Vison 29, 5–28 (1998)
Klein, J., Lecomte, C., Miche, P.: Preceding car tracking using belief functions and a particle filter. In: IEEE ICPR - International Conference on Pattern Recognition, pp. 1–4 (2008)
Lu, W.-L., Okuma, K., Little, J.J.: Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image Vision Comput. 27(1-2), 189–205 (2009)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Int. Joint Conf. Artif. Intel., IJCAI 1981, vol. 2, pp. 674–679. Morgan Kaufmann Publishers Inc., San Francisco (1981)
Neidle, C., Sclaroff, S., Athitsos, V.: Signstream: A tool for linguistic and computer vision research on visual-gestural language data. Behav. Res. Meth. Ins. C 33(3), 311–320 (2001)
Perez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking with particles. Proceedings of the IEEE, 495–513 (2004)
Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House (2004)
Shan, C., Tan, T., Wei, Y.: Real-time hand tracking using a mean shift embedded particle filter. PR 40(7), 1958–1970 (2007)
Sigal, L., Sclaroff, S., Athitsos, V.: Skin color-based video segmentation under time-varying illumination. PAMI 26, 862–877 (2003)
Stokoe, W.C.: Sign language structure: An outline of the visual communication systems of the american deaf. Journal of Deaf Studies and Deaf Education 10(1) (2005)
Viola, P., Jones, M.J.: Robust Real-Time face detection. Int. J. Comput Vison 57(2), 137–154 (2004)
Yang, S., Ge, W., Cheng, Z.: Detecting and tracking moving targets on omnidirectional vision. Transactions of Tianjin University 15(1), 13–18 (2009)
Yao, A., Uebersax, D., Gall, J., Van Gool, L.: Tracking People in Broadcast Sports. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 151–161. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Belgacem, S., Chatelain, C., Ben-Hamadou, A., Paquet, T. (2012). Hand Tracking Using Optical-Flow Embedded Particle Filter in Sign Language Scenes. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)