Commenced in January 2007
Paper Count: 30184
A Robust Method for Hand Tracking Using Mean-shift Algorithm and Kalman Filter in Stereo Color Image Sequences
Abstract:Real-time hand tracking is a challenging task in many computer vision applications such as gesture recognition. This paper proposes a robust method for hand tracking in a complex environment using Mean-shift analysis and Kalman filter in conjunction with 3D depth map. The depth information solve the overlapping problem between hands and face, which is obtained by passive stereo measuring based on cross correlation and the known calibration data of the cameras. Mean-shift analysis uses the gradient of Bhattacharyya coefficient as a similarity function to derive the candidate of the hand that is most similar to a given hand target model. And then, Kalman filter is used to estimate the position of the hand target. The results of hand tracking, tested on various video sequences, are robust to changes in shape as well as partial occlusion.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073597Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2531
 N. Liu, and B. C. Lovell, MMX-accelerated Real-Time Hand Tracking System, Proceedings of Image and Vision Computing, pp. 381-385, 2001.
 D. B. Nguyen, S. Enokida, and E. Toshiaki, Real-Time Hand Tracking and Gesture Recognition System, International Conference on Graphics, Vision and Image Processing, CICC, pp. 362-368, 2005.
 T. Nobuhiko, S. Nobutaka, and S. Yoshiaki, Extraction of Hand Features for Recognition of Sign Language Words, International Conference on Vision Interface, pp. 391-398, 2002.
 D. Comaniciu, V. Ramesh, and P. Meer, Kernel-Based Object Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 564-577, 2003.
 D. Comaniciu, V. Ramesh, and P. Meer, Real-Time Tracking of Non-Rigid Objects Using Mean Shift, Conference on CVPR, Vol. 2, pp. 1-8, 2000.
 G. Welch, and G. Bishop, An Introduction to the Kalman Filter, In Technical Report, University of North Carolina at Chapel Hill, pp. 95- 041, 1995.
 M. Elmezain, A. Al-Hamadi, and B. Michaelis, Real-Time Capable System for Hand Gesture Recognition Using Hidden Markov Models in Stereo Color Image Sequences, Journal of WSCG, Vol. 16, No. 1, pp. 65-72, 2008.
 M. Elmezain, A. Al-Hamadi, and B. Michaelis, A Novel System for Automatic Hand Gesture Spotting and Recognition in Stereo Color Image Sequences, Journal of WSCG, Vol.17, No. 1, pp. 89-96, 2009.
 M. Elmezain, A. Al-Hamadi, J. Appenrodt, and B. Michaelis, A Hidden Markov Model-Based Continuous Gesture Recognition System for Hand Motion Trajectory, International Conference on Pattern Recognition (ICPR), pp. 519-522, 2008.
 M. Elmezain, A. Al-Hamadi, and B. Michaelis, Spatio-Temporal Feature Extraction-Based Hand Gesture Recognition for Isolated American Sign Language and Arabic Numbers, IEEE Symposium on Image and Signal Processing and Analysis (ISPA), pp. 254-259, 2009.
 R. Klette, K. SChl┬¿uns, and A. Koschan, Computer Vision: Three- Dimensional Data from Images, Springer, Singapore, ISBN 981-3083- 71-9, 1998.
 R. Niese, A. Al-Hamadi, and B. Michaelis, A Novel Method for 3D Face Detection and Normalization, Journal of Multimedia, Vol. 2, pp. 1-12, 2007.
 S. Khalid, U. Ilyas, S. Sarfaraz, and A. Ajaz, ABhattacharyya Coefficient in Correlation of Gary-Scale Objects, Journal of Multimedia, Vol. 1, pp. 56-61, 2006.
 D. Comaniciu, and P. Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, pp. 603-619, 2002.