Kernel-Based Hand Tracking

In this work, a new method is proposed for hand tracking based on a density approximation and optimization method. Considering tracking as a classification problem, we train an approxiator to recognize hands from its background. This procedure is done by extracting feature vector of every pixel in the first frame and then building an approximator to construct a virtual optimized surface of pixels for similarity of the frames which belong to the hand of those frames related to the movie. Received a new video frame, approximator is employed to test the pixels and build a surface. In this method, the features we use is color RGB corresponding to the feature space. Conducting simulations, it is demonstrated that hand tracking based on this method result in acceptable and efficient performance. The experimental results agree with the theoretical results. Key word: Hand Tracking, Kernel Density, Approximator.

[1]  Daniel P. Huttenlocher,et al.  Adaptive Bayesian recognition in tracking rigid objects , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[3]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[4]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..

[6]  James M. Rehg,et al.  A multiple hypothesis approach to figure tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  Ingemar J. Cox,et al.  An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Alessio Del Bue,et al.  Smart cameras with real-time video object generation , 2002, Proceedings. International Conference on Image Processing.

[9]  Hwann-Tzong Chen,et al.  Trust-region methods for real-time tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[11]  Luc Van Gool,et al.  Real-time affine region tracking and coplanar grouping , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Xosé R. Fernández-Vidal,et al.  Information Theoretic Measure for Visual Target Distinctness , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[14]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[16]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[17]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[18]  Neil A. Thacker,et al.  The Bhattacharyya metric as an absolute similarity measure for frequency coded data , 1998, Kybernetika.

[19]  Alan L. Yuille,et al.  Fundamental bounds on edge detection: an information theoretic evaluation of different edge cues , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[20]  David J. Fleet,et al.  Probabilistic Detection and Tracking of Motion Boundaries , 2000, International Journal of Computer Vision.

[21]  Shai Avidan,et al.  Support Vector Tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[23]  F. D. Garber,et al.  The Quality of Training Sample Estimates of the Bhattacharyya Coefficient , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  David W. Scott,et al.  Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[25]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[26]  L. Van Gool,et al.  Analyzing the layout of composite textures , 2002 .

[27]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[28]  Dimitris N. Metaxas,et al.  Optical Flow Constraints on Deformable Models with Applications to Face Tracking , 2000, International Journal of Computer Vision.

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

[30]  Gérard G. Medioni,et al.  Finding Waldo, or focus of attention using local color information , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Rachid Deriche,et al.  Region tracking through image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.

[32]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[33]  Thomas S. Huang,et al.  JPDAF based HMM for real-time contour tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.