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Recognition and incremental learning of scenario-oriented human behavior patterns by two threshold models

Published: 06 March 2011 Publication History

Abstract

Two HMM-based threshold models are suggested for recognition and incremental learning of scenario-oriented human behavior patterns. One is the expected behavior threshold model to discriminate if a monitored behavior pattern is normal or not. The other model is the registered behavior threshold model to detect whether such behavior pattern is already learned. If a behavior patten is detected as a new one, an HMM is generated to represent the pattern, and then the HMM is used to update behavior clusters by hierarchical clustering process.

References

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D. Kulić and Y. Nakamura. Incremental learning of full body motion primitives. From Motor Learning to Interaction Learning in Robots, pages 383--406, 2010.
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H. Lee and J. Kim. An HMM-based threshold model approach for gesture recognition. IEEE Transactions on pattern analysis and machine intelligence, 21(10):961--973, 1999.
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G. H. Lim, K. Kim, B. Chung, I. H. Suh, and H. Suh. Service-oriented Context Reasoning Incorporating Patterns and Knowledge for Understanding Human-augmented Situations. In Proceedings of the IEEE Ro-Man 2010, 2010.
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G. H. Lim, I. H. Suh, and H. Suh. Ontology-based Unified Robot Knowledge for Service Robots in Indoor Environments. IEEE Transactions on Systems, Man, and Cybernetics, Part A, accepted for publication.
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S. Schaal, A. Ijspeert, and A. Billard. Computational approaches to motor learning by imitation. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 358(1431):537, 2003.

Cited By

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  • (2024)Labelling a Stereo Event Dataset in Indoor Scenes for Segmentation Tasks2024 21st International Conference on Ubiquitous Robots (UR)10.1109/UR61395.2024.10597458(619-623)Online publication date: 24-Jun-2024
  • (2016)Two-step learning about normal and exceptional human behaviors incorporating patterns and knowledge2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)10.1109/MFI.2016.7849483(162-167)Online publication date: Sep-2016

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Published In

cover image ACM Conferences
HRI '11: Proceedings of the 6th international conference on Human-robot interaction
March 2011
526 pages
ISBN:9781450305617
DOI:10.1145/1957656

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In-Cooperation

  • RA: IEEE Robotics and Automation Society
  • Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc
  • The Association for the Advancement of Artificial Intelligence (AAAI)
  • IEEE Systems, Man and Cybernetics Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 March 2011

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Author Tags

  1. behavior pattern recognition
  2. hidden markov model
  3. incremental learning
  4. threshold model

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Overall Acceptance Rate 268 of 1,124 submissions, 24%

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View all
  • (2024)Labelling a Stereo Event Dataset in Indoor Scenes for Segmentation Tasks2024 21st International Conference on Ubiquitous Robots (UR)10.1109/UR61395.2024.10597458(619-623)Online publication date: 24-Jun-2024
  • (2016)Two-step learning about normal and exceptional human behaviors incorporating patterns and knowledge2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)10.1109/MFI.2016.7849483(162-167)Online publication date: Sep-2016

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