Hidden Markov model based continuous online gesture recognition

Presents the extension of an existing vision-based gesture recognition system using hidden Markov models(HMMs). Several improvements have been carried out in order to increase the capabilities and the functionality of the system. These improvements include position independent recognition, rejection of unknown gestures, and continuous online recognition of spontaneous gestures. We show that especially the latter requirement is highly complicated and demanding, if we allow the user to move in front of the camera without any restrictions and to perform the gestures spontaneously at any arbitrary moment. We present solutions to this problem by modifying the HMM-based decoding process and by introducing online feature extraction and evaluation methods