クリスタルボール : 運動の記号推論を通じた未来の行動予測

概要

論文の詳細を見る
This paper describes an approach to structuring behavioral knowledge based on symbolization of human whole body motions, hierarchical classification of the motions, and extraction of the causality among the motions. The motion patterns are symbolized by Hidden Markov Models (HMMs), which can be used for recognition of the motion patterns. The HMMs are called “motion symbol” since they abstract their corresponding motion patterns. The motion patterns are organized into a hierarchical tree structure (“motion symbol tree”) representing the property of spatial similarity among the motion patterns. The motion patterns are classified based on the motion symbol tree. The sequences of the motion patterns are stochastically represented as transitions between the abstracted motion patterns by using an N-gram model (“motion symbol graph”), and the temporal causality among the human behaviors are extracted. The integration of the motion symbol tree and the motion symbol graph makes it possible to recognize motion patterns fast and predict human behavior during observation as if the behaviors in the future are reflected in a Crystal Ball. The experiments on a large motion dataset validate the proposed framework.
2011-10-15