A mixed-state condensation tracker with automatic model-switching

There is considerable interest in the computer vision community in representing and modelling motion. Motion models are used as predictors to increase the robustness and accuracy of visual trackers, and as classifiers for gesture recognition. This paper presents a significant development of random sampling methods to allow automatic switching between multiple motion models as a natural extension of the tracking process. The Bayesian mixed-state framework is described in its generality, and the example of a bouncing ball is used to demonstrate that a mixed-state model can significantly improve tracking performance in heavy clutter. The relevance of the approach to the problem of gesture recognition is then investigated using a tracker which is able to follow the natural drawing action of a hand holding a pen, and switches state according to the hand's motion.

[1]  Aaron F. Bobick,et al.  A state-based technique for the summarization and recognition of gesture , 1995, Proceedings of IEEE International Conference on Computer Vision.

[2]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[3]  Ryuichi Oka,et al.  A theoretical consideration of pattern space trajectory for gesture spotting recognition , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[4]  Stephen M. Omohundro,et al.  Nonlinear manifold learning for visual speech recognition , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  A. Pentland,et al.  Toward augmented control systems , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[6]  Andrew Blake,et al.  Learning Dynamics of Complex Motions from Image Sequences , 1996, ECCV.

[7]  Thad Starner,et al.  Visual Recognition of American Sign Language Using Hidden Markov Models. , 1995 .

[8]  Daniel E. Koditschek,et al.  Dynamical system representation, generation, and recognition of basic oscillatory motion gestures , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[9]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[10]  Michael Isard,et al.  Learning to Track the Visual Motion of Contours , 1995, Artif. Intell..

[11]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .