Bidirectional estimation between context and motion in motion sequence in which context changes
Tadashi Ogura, Tetsunari Inamura · Advanced Robotics · 2019
We describe a motion recognition method for reducing recognition errors. The method has a two-layer structure: a lower layer for motion recognition that is affected by the distribution of topics used as context information in the upper layer and an upper layer for the topic distribution that is affected by motion recognition in the bottom layer. We introduce an algorithm for the bottom layer to integrate the motion likelihood calculated using a hidden Markov model and motion appearance probabilities obtained by a topic model. We also use a set of particles to estimate and update contexts on the basis of the result of motion recognition in the bottom layer. The set of particles presents a probabilistic distribution of motion topics, and motion recognition and particle update procedures are performed on each particle. We experimented with two types of sequential motion: a sequence of 33 daily motions and complex motion sequences assuming actual observation. The results showed that the proposed method reduced recognition errors and tracked latent topics better than conventional methods did.