Extraction of Human Activities as Action Sequences using pLSA and PrefixSpan

In this paper, we propose a framework for recognizing human activities in our daily life. Since a human activity is represented as a sequence of actions, the actions are recognized from videos and then the frequently-occurring human activities can be extracted from them. We show the experimental results applied to the data taken in a deskwork environment to demonstrate the performance of the proposed framework. The experimental results were as follows: 86.0% averaged recall rate and 78.3% averaged precision rate were obtained in extracting human activities.