人間行動の統計的モデリングのための遅れを伴う状況-行動間の因果関係パターン抽出手法

Abstract

SUMMARY A modeling approach for human action is the focus of this paper. We design a human action model based on the stored data obtained during long-term monitoring of a person. This approach consists of the following two processes. First, several kinds of partial time series data that occur frequently are extracted from the stored data and taken to be human action patterns. Next, the extracted time series data are modeled based on a statistical modeling method such as the hidden Markov model. In this research, we focus on the extraction method for the time series data that occur frequently in the stored data. A person changes his actions according to changes in the situation around him. Moreover, it takes some time for him to perform his action after he recognizes the situation around himself. This time is called the delay time in this paper. A human action model that takes this delay time into consideration leads to greater accuracy in recognition and prediction of human actions based on that action. It is necessary to extract time series data for a situation and an action with the delay time as training data in order to generate the above human action model. In conventional methods, multidimensional time series data are used as the stored data without a distinction between the situation and action data. Also, some partial time series data that occur frequently are extracted from the stored data. Therefore, the delay time is not taken into account. In this paper, we propose a new extraction method for time series data that occur frequently with the time delay taken into consideration. In this method, sets of partial time series data that occur frequently with the delay time are extracted by evaluating the repeatability and similarity between the partial time series data sets with different occurrence frequencies. In the experiment, we extract the interaction motions that occur frequently between two subjects. The utility of the proposed method is examined based on the experimental results.