Bayesian Fusion of Hidden Markov Models for Understanding Bimanual Movements

Understanding hand and body gestures is a part of a wide spectrum of current research in computer vision and Human-Computer Interaction. A part of this can be the recognition of movements in which the two hands move simultaneously to do something or imply a meaning. We present a Bayesian network for fusing Hidden Markov Models in order to recognise a bimanual movement. A bimanual movement is tracked and segmented by a tracking algorithm. Hidden Markov Models are assigned to the segments in order to learn and recognize the partial movement within each segment. A Bayesian network fuses the HMMs in order to perceive the movement of the two hands as a single entity.

• Type: Conference
• Year: 2004
• Where: FGR
• Authors: Atid Shamaie, Alistair Sutherland
• Keywords: Bayesian Network, Bimanual Movement, Biometrics, FGR 2004, Hidden Markov Models