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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
編集者Maria De De Marsico, Gabriella Sanniti di Baja, Ana Fred
出版社SciTePress
ページ676-683
ページ数8
2017-January
ISBN(電子版)9789897582226
DOI
出版ステータス出版済み - 1 1 2017
イベント6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, ポルトガル
継続期間: 2 24 20172 26 2017

出版物シリーズ

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

会議

会議6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Countryポルトガル
CityPorto
Period2/24/172/26/17

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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