An HMM-based approach for gesture segmentation and recognition

Gesture, as a "natural" means, provides an alternative way for human-computer interaction. The recognition of continuous gestures suffers greatly from the existence of non-gesture hand motions. The given gestures can start at any moment in an input sequence. The Hidden Markov model (HMM) is used to tackle this problem. The paper proposes a method for the spotting and recognition of continuous spatio-temporal features. Without sliding the input temporal patterns past the trained models, the algorithm makes use of accumulation scores for evaluation. So it is an exhaustive evaluation method but only a sum operation is needed in each input frame. The method is demonstrated with real experiments on the recognition of some spatio-temporal trajectories. Results of the experiments show that the proposed method is very effective and fast in extracting given gestures from a continuous trajectory containing non-gestures.