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

G.H. Lim, B. Chung, I.H. Suh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationHRI 2011 - Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction
Pages189-190
Number of pages2
DOIs
Publication statusPublished - 2011
Event6th ACM/IEEE International Conference on Human-Robot Interaction - Lausanne, Switzerland
Duration: 6 Mar 20119 Mar 2011
Conference number: 84437

Conference

Conference6th ACM/IEEE International Conference on Human-Robot Interaction
Abbreviated titleHRI 2011
Country/TerritorySwitzerland
CityLausanne
Period6/03/119/03/11

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