- Volume 10 Issue 11
The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION. The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation rule of conditional probability propagation. We used two models for the evaluation of particle Inter and apply the MATLAB for the preprocessing of the image sequence. But we implement the particle filter using the C++ to get the high speed processing. In the experimental results, it is demonstrated that the proposed algorithm prove to be robust in the cluttered environment.
- M. Isard and A. Blake, 'CONDENSATION - conditional density propagation for visual tracking', Int. J. Computer Vision, 1998
- Michael J. Black and Allan D. Jepson, 'A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions', In Proceedings 5th European Conf. Computer Vision, Vol. 1, pp. 909-924, 1998 https://doi.org/10.1007/BFb0055712
- Michael Isard and Andrew Blake, 'A mixed-state condensation tracker with automatic model-switching,' In Proceedings 6th Internal Conf. computer Vision, pp. 107-112, 1998