A Real-Time Gesture Tracking and Recognition System Based on Particle Filtering and Ada-Boosting Techniques
A real-time gesture tracking and recognition system based on particle filtering and Ada-Boosting techniques is presented in this paper. The particle filter, which is a flexible simulation-based method and suitable for non-linear tracking problems, is adopted to achieve hand tracking robustly. In order to avoid the influence of the other exposed skin parts of a human body and skin-colored objects in the background, our system further applies the motion information as a feature of the hand in addition to the skin color information. Compared with the conventional particle filters, our method leads to more efficient sampling and requires fewer particles. It results in lowering computational cost and saving much time for gesture recognition later. The gesture recognition uses the features derived from the wavelet transform, and employs an Ada-Boost algorithm which is excellent in facilitating the speed of convergence during the training. Hence, it is conducive to update new information and expand new gesture archives. The experimental results reveal our system is fast, accurate, and robust in hand tracking. Moreover, it has good performance in gesture recognition under complicated environments.
Unable to display preview. Download preview PDF.
- 1.Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal 2(2), 1–15 (1998)Google Scholar
- 2.Imagawa, K., Lu, S., Igi, S.: Color-based hands tracking system for sign language recognition. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 462–467 (1998)Google Scholar
- 3.Shan, C., Wei, Y., Tan, T., Ojardias, F.: Real time hand tracking by combining particle filtering and mean shift. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 669–674 (2004)Google Scholar
- 4.Liu, X., Fujimura, K.: Hand gesture recognition using depth data. In: Proceedings of the 6th IEEE Intern. Conf. on Automatic Face and Gesture Recognition, pp. 529–534 (2004)Google Scholar
- 5.Song, X.Q.: Real-time visual detection and tracking of multiple moving objects based on particle filtering techniques, Master Thesis, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC (2005)Google Scholar
- 6.Kumar, S., Kumar, D.K., Sharma, A., McLachlan, N.: Classification of visual hand movements using multiresolution wavelet images. In: Proceedings of the International Conference on Intelligent Sensing and Information Processing, pp. 373–378 (2004)Google Scholar
- 8.Graf, H.P., Cosatto, E., Gibbon, D., Kocheisen, M., Petajan, E.: Multi-modal system for locating heads and faces. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 88–93 (1996)Google Scholar
- 10.Vezhnevets, A.: GML Ada-Boost Matlab Toolbox, Technique Manual, Graphics and Media Laboratory, Computer Science Department, Moscow State University, Moscow, Russian Federation (2006), http://research.graphicon.ru/