Keywords:Human Motion Recognition
It is generally difficult to recognize and distinguish human motion which are different kinds of motions but whose motion patterns are similar to each other. Conventionally, we focused on the context of motion and proposed a method of alternately performing two processes, motion recognition using context information and re-estimation of context based on recognition results. However, in our previous works, the performance evaluation of detailed algorithms such as reasonable repetition times in these two recognition processes was not discussed. In addition, only an unrealistic ideal distribution is used as the motion appearance probability for predicting motions that may be performed based on the current context, and utility in the probability distribution including noise has not been discussed. In this paper, we aim to investigate these problems and clarify the conditions for performance improvement. Through experiments, a high motion recognition ratio was obtained when the number of iterations was 5 and 10. Furthermore, we confirmed that the proposed method maintains a high motion recognition ratio even if using noisy motion appearance probability. From these results, we have concluded that the proposed method has utility not only for ideal conditions but also for practical motion patterns.