Modeling and recognition of hand gesture using colored Petri nets

The main characteristics of human hand gestures can be summarized by their dynamic, multiattribute property. To utilize hand gestures as a way of interaction, it is necessary to analyze the motion patterns for each of the gesture attributes and finally to extract the whole interpretation by integrating the relevant factors across time. Previous research has shown the possibility for recognition of local aspects of hand gesture. But the global framework for finding the whole interpretation from the local aspects has yet to be provided. We propose a colored Petri net model for high-level description of hand gestures. This model intercommunicates with simultaneous low-level recognizers and thus finds a whole-interpretation for the gesture