Bayesian classification of task-oriented actions based on stochastic context-free grammar

This paper proposes a new approach for recognition of task-oriented actions based on stochastic context-free grammar (SCFG). Our attention puts on actions in the Japanese tea ceremony, where the action can be described by context-free grammar. Our aim is to recognize the action in the tea services. Existing SCFG approach consists of generating symbolic string, parsing it and recognition. The symbolic string often includes uncertainty. Therefore, the parsing process needs to recover the errors at the entry process. This paper proposes a segmentation method errorless as much as possible to segment an action into a string of finer actions. This method, based on an acceleration of the body motion, can produce the fine action corresponding to a terminal symbol with little error. After translating the sequence of fine actions into a set of symbolic strings, SCFG-based parsing of this set leaves small number of ones to be derived. Among the remaining strings, Bayesian classifier answers the action name with a maximum posterior probability. Giving one SCFG rule the multiple probabilities, one SCFG can recognize multiple actions