HHMM based recognition of human activity from motion trajectories in image sequences

Daiki Kawanaka, Shun Ushida, Takayuki Okatani, Koichiro Deguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we present a method for recognition of human activity as a series of actions from an image sequence. The difficulty with the problem is that there is a chicken-egg dilemma that each action needs to be extracted in advance for its recognition but the precise extraction is only possible after the action is correctly identified. In order to solve this dilemma, we use as many models as actions of our interest, and test each model against a given sequence to find a matched model for each action occurring in the sequence. For each action, a model is designed so as to represent any activity containing the action. The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Several experimental results are shown.

Original languageEnglish
Title of host publicationProceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005
Pages578-581
Number of pages4
Publication statusPublished - 2005 Dec 1
Event9th IAPR Conference on Machine Vision Applications, MVA 2005 - Tsukuba Science City, Japan
Duration: 2005 May 162005 May 18

Publication series

NameProceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005

Other

Other9th IAPR Conference on Machine Vision Applications, MVA 2005
CountryJapan
CityTsukuba Science City
Period05/5/1605/5/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Kawanaka, D., Ushida, S., Okatani, T., & Deguchi, K. (2005). HHMM based recognition of human activity from motion trajectories in image sequences. In Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005 (pp. 578-581). (Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005).