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Tracking Complex Movements under Assorted Backgrounds in Sign Language using Active Contour Models

P.V.V. Kishore, Dr. P. Rajesh Kumar

Abstract


 

This paper deals with an important issue related to gesture recognition systems. Three important characteristics of sign language are hand and head orientation, location and movement apart from hand shapes. To employ these three characteristics into a sign language recognition system we engaged active contours for tracking hands and head in sign language videos. The hands tracking is accomplished by fusing skin color, texture, boundary and shape information. From RGB (Red, Blue, Green) color space used to model skin color, a single color plane is extracted based on the video background. The texture information is computed using a statistical co-occurrence matrix. The boundary information is computed by calculating the divergence vector on the extracted color and texture feature vector. The shape is computed dynamically and is made adaptive to each video frame to track hands and head during occlusions and in complex video backgrounds. The tracking is achieved using level sets energy minimization on each video frame. The performance of our tracking model is illustrated by tracking hands of the signer in image sequences under simple backgrounds, natural backgrounds and complex backgrounds.


Keywords


Active Contours, Skin Color, Texture, Boundary, Shape Modules, Gaussian Mixture Models, Sign Language, Hands and Head Tracking.

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References


P.V.V.Kishore, P.Rajesh Kumar, E.Kiran Kumar & S.R.C.KIshore . ―Video Audio Interface for Recognizing Gestures of Indian Sign Language‖ International Journal of Image Processing (IJIP), CSC Journals, Sep 2011,Vol. 5, No.4, pp479-503,.

P.V.V.Kishore, P.Rajesh Kumar, A.Arjuna Rao, ‖Static Video Based Visual-Verbal Exemplar for Recognizing Gestures of Indian Sign Language‖ International Journal of Digital Image Processing, CiiT Journals, May 2011,Vol. 3, No.9, pp530-537.

W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.

Imagawa I et. al, ‗Recognition of local features for camera-based sign language recognition system‘, Proc. ICPR‘00, pp849-853, 2000.

Yang M, Ahuja N, ‘Recognizing hand gesture using motion trajectories‘,CVPR‘99, pp892-897, 1999.

Hong P et.al. ―Gesture modeling and recognition using finite state machines‖, IEEE International Conference on Automatic Face and Gesture Recognition, pp410 –415, 2000.

B. Stenger, A. Thayananthan, P. Torr, and R. Cipolla.Model-based hand tracking using a hierarchical bayesian filter. IEEE transactions on pattern analysis and machine intelligence, 28(9):1372–1384, 2006.

Dewaele, G. and Devernay, F. and Horaud, R. and Forbes, F. The alignment between 3-d data and articulated shapes with bending surfaces. Proceedings of the 9th European Conference on Computer Vision, Graz, pages 578–591, 2006.

Michael Donoser and Horst Bischof. Real Time Appearance Based Hand Tracking. In Proceedings of International Conference on Pattern Recognition (ICPR), 2008

Shan Lu, D. Metaxas, D. Samaras and J. Oliensis, ―Using multiple cues for hand tracking and model refinement‖, IEEE Conf. on Computer Vision and Pattern Recognition 2003, 18-20 Jun. 2003, vol.2, pp. 443-450.

Xiying Wang, Xiwen Zhang and Guozhong Dai. Tracking of Deformable Human Hand in Real Time as Continuous Input for Gesturebased Interaction, 2007 International Conference on Intelligent User Interfaces (IUI 2007), Hawaii, USA.

K. Imagawa, S. Lu, and S. Igi, ―Color-Based Hand Tracking System for Sign Language Recognition,‖ Proc. Int‘l Conf. Automatic Face and Gesture Recognition, pp. 462-467, 1998.

L. Brkthes, P. Menezes, E Lerasle and J. Hayet ―Face tracking and hand gesture recognition for human-robot interaction‖ Pmcwdingr of the 2004 IEEE InternatIona1 Confennee on RobotIco 6 Automation New Orleans. April 2004.

Blake. A, Isard. M,Active Contours. Springer, Heidelberg (1998).

Chan.T, Vese.L.A, Active contours without edges. IEEE Trans. Image Process. Vol.10,no.2,2001, 266–277.

Paragios. N, Deriche.R, Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. Vol. 22,no.3, 2000, 266–280.

Paragios. N, Deriche. R, Geodesic active regions and level set methods for motion estimation and tracking. Comput. Vis. Image Understand. Vol. 97, no.3, 2004, 259–282.

Mansouri.A.R, Region tracking via level set pdes without motion computation. IEEE Trans. Pattern Anal. Mach. Intell. 2002, Vol.24, no.7, 947–961.

Jehan-Besson, S, Barlaud, M.,Aubert,G.: Detection and tracking of moving objects using a new level set based method. Proceedings of IEEE International Conference on Pattern Recognition, Barcelona, Spain, 3–8 September, 2000, pp. 7112–7117.

Cremers, D, Dynamical statistical shape priors for level set-based tracking. IEEE Trans. Pattern Anal. Mach. Intell, 2006, Vol. 28,no. 8, pp.1262–1273.

M.Kass, A Witkin, D Terzopoulos ,―Snakes:active Contour Models‖, Int. J. of Computer Vision, 1987,pp 321-331.

Osher, S., Sethian, J. Fronts propagating with curvature-dependant speed: algorithms based on Hammilton-Jacobi formulations.J. Comput. Phys. Vol.79 No.1,1988, pp12–49.

R. Malladi, J.A. Sethian, and B.C. Vemuri. ―Shape modeling with front propagation: A level set approach‖. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.17 No. 2, 1995, pp158-175.

Tuceryan, M., Jain, A, Texture analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds) The Handbook of Pattern Recognition and Computer Vision, 2nd edn. chap. 2.1, 207–248.World Scientific Publishing, Singapore.

Allili, M.S., Ziou, D.,(2006), ―Automatic color-texture image segmentation by using active contours‖. In: Proceedings of 1st IEEE International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, Xi‘an, China, 26–27, LNCS 4153, pp. 495–504.

S. Allam, M. Adel, P. Refregier, ―Fast algorithm for texture discrimination by use of a separable orthonormal decomposition of the co-occurrence matrix,‖ Applied Optics, vol.36, 1997, pp.8313–8321.

R. M. Haralick, K. Shangmugam, I. Dinstein, (1973) ―Textural Feature for Image Classification,‖ IEEE Trans on Systems, Man, Cybernetics, 3(6), pp.610—621.

McLachlan, G., Peel, D, Finite Mixture Models, Wiley Series in Probability and Statistics (2000).

A Dempster, N. Laird, and D. Rubin. ―Maximum likelihood from incomplete data via the EMalgorithm,‖ Journal of the Royal Statistical Society, vol. 39, 1977, 1-38.

Allili, M.S., Ziou, D.: Automatic color-texture image segmentation by using active contours. In: Proceedings of 1st IEEE International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, Xi‘an, China, 26–27, August 2006, LNCS 4153, pp. 495–504.

Cremers, D., Soatto, S.: A pseudo-distance for shape priors in level set segmentation. In: Proceedings of 2nd IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision, Nice, France, 13–16 October 2003, pp169–176.

Ketut Fundana · Niels C. Overgaard · Anders Heyden,(2008), ―Variational Segmentation of Image Sequences Using Region-Based Active Contours and Deformable Shape Priors‖ Int J Comput Vis vol.80: pp289–299.

Adalsteinsson, D., Sethian, J, ―A fast level set method for propagating surfaces‖. J. Comput. Phys. 118 vol (2), 1998, pp269–277.

Weickert, J., Kuhne, G.: Fast methods for implicit active contour models. In: Osher, S., Paragios, N.: (eds) Geometric Level Set Methods in Imaging, Vision and Graphics, chap. 3, pp. 44–57. Springer, Heidelberg.

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