An HMM-based approach for gesture segmentation and recognition

Gesture, as a "natural" mean, provides an alternative way for human-computer interaction. The recognition of continuous gestures suffers greatly from the existences of non-gesture hand motions. The given gestures can start at any moment in an input sequence. Hidden Markov Model (HMM) is used to tackle this problem. This paper proposes a novel 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.