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Analysis of Nursery School Observations for Understanding Children’s Behavior

  • Jien Kato
  • Yu Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7231)

Abstract

This paper introduces an on-going project with the goal of measuring and analyzing children’s behavior automatically. Some key technologies, including methodologies for acquiring data, tracking a target across different cameras over time, activity recognition, interaction analysis, and behavior summarization for a target child are presented. Some encouraging results from a real system we developed in a nursery school environment are also described. As these technologies enable the content-based retrieval, comparison, and summarization of large-scale observational data, they are applicable to various purposes, such as the assessment of children’s development, healthcare and diagnosis.

Keywords

activity recognition interaction analysis video summa-rization behavior quantization distance metric learning 

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References

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    Ishikawa, T., Wang, Y., Kato, J.: Daily Digest Generation of Kindergartner from Surveillance Video. IEEJ Transactions on Electronics, Information and Systems 131(2), 385–392 (2011) (in Japanese)CrossRefGoogle Scholar
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    Kato, J., Ishikawa, T., Wang, Y., Ishii, K.: Video Digest Generation with Multisensor for Nursery School Kids. ICIC Express Letters 5(6), 1921–1927 (2011)Google Scholar
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    Weinberger, K.Q., Saul, K.: Distance Metric Learning for Large Margin Nearest Neighbour Classification. Journal of Machine Learning Research 10, 207–244 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jien Kato
    • 1
  • Yu Wang
    • 1
  1. 1.Dept. of Systems and Social Informatics Graduate School of Information ScienceNagoya UniversityNagoyaJapan

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