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Extraction of Human Activities as Action Sequences using pLSA and Prefix Span

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Activity representation
 3. Approach
  3.1. Human action categorizing method
  3.2. Extraction of activities
 4. Experimental results
  4.1. Experimental conditions
  4.2. Experimental results
 5. Conclusion
 6. References

저자정보

  • Takuya TONARU Graduate School of Engineering, Kobe University
  • Tetsuya TAKIGUCHI Organization of Advanced Science and Technology, Kobe University
  • Yasuo ARIKI Organization of Advanced Science and Technology, Kobe University

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