Deep learning-based prediction method for people flows and their anomalies

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

1 Citation (Scopus)

Abstract

This paper proposes prediction methods for people flows and anomalies in people flows on a university campus. The proposed methods are based on deep learning frameworks. By predicting the statistics of people flow conditions on a university campus, it becomes possible to create applications that predict future crowded places and the time when congestion will disappear. Our prediction methods will be useful for developing applications for solving problems in cities.

Original languageEnglish
Title of host publicationICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages676-683
Number of pages8
Volume2017-January
ISBN (Electronic)9789897582226
DOIs
Publication statusPublished - Jan 1 2017
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: Feb 24 2017Feb 26 2017

Publication series

NameICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
Volume2017-January

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
CountryPortugal
CityPorto
Period2/24/172/26/17

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Takano, S., Hori, M., Goto, T., Uchida, S., Kurazume, R., & Taniguchi, R. I. (2017). Deep learning-based prediction method for people flows and their anomalies. In M. D. De Marsico, G. S. di Baja, & A. Fred (Eds.), ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (Vol. 2017-January, pp. 676-683). (ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods; Vol. 2017-January). SciTePress. https://doi.org/10.5220/0006248806760683