Abstract
This paper presents a system for estimating indoor human behavior using laser range finders on the floor. The proposed method uses hierarchical hidden Markov model(H-HMM) composed of an action estimate layer and a behavior estimate layer. The former is constructed by two kinds of HMMs: one is the HMM for estimating each action, and the other is the HMM for deciding the human action considering the action continuity. In the latter layer, one HMM learns each behavior by using as the features the relative relationship among the actions and the furniture. Our behavior estimation using such features enable to recognize the behaviors robustly even thought the indoor environment is changed.
