Hand Gesture Modeling Using Dynamic Bayesian Networks and Deformable Templates

The paper presents a stochastic approach to articulated hand (palm shape) tracking in images. The gesture model is given in terms of a Dynamic Bayesian network that incorporates a Hidden Markov Model in order to utilize prior information on gesture structure in the tracking task. The Deformable Templates methodology is applied for hand shape modeling. Experimental evaluation of articulated hand tracking in cluttered environment using particle filtering is provided. A comparison of this method with a typical tracking approach, that makes no use of temporal gesture information, is also given.

[1]  Michael Isard,et al.  Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion , 2000 .

[2]  Włodzimierz Kasprzak,et al.  Constrained contour matching in hand posture recognition , 2009 .

[3]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[4]  Frédéric Lerasle,et al.  DBN versus HMM for Gesture Recognition in Human-Robot Interaction , 2009 .

[5]  Chung-Lin Huang,et al.  A model-based hand gesture recognition system , 2001, Machine Vision and Applications.

[6]  Steve Young,et al.  HMMs and related speech recognition technologies , 2008 .

[7]  Ramesh C. Jain,et al.  Recursive identification of gesture inputs using hidden Markov models , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[8]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[9]  Aaron F. Bobick,et al.  Recognition and interpretation of parametric gesture , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[10]  Seong-Whan Lee,et al.  Recognizing hand gestures using dynamic Bayesian network , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[11]  Narendra Ahuja,et al.  Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Kevin Murphy,et al.  Switching Kalman Filters , 1998 .

[13]  Huang Fei,et al.  A hybrid HMM/particle filter framework for non-rigid hand motion recognition , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[14]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[15]  Björn Stenger,et al.  Model-based hand tracking using a hierarchical Bayesian filter , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[17]  Chung-Lin Huang,et al.  Hand gesture recognition using a real-time tracking method and hidden Markov models , 2003, Image Vis. Comput..

[18]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  Vladimir Pavlovic,et al.  Dynamic bayesian networks for information fusion with applications to human-computer interfaces , 1999 .