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

Shigeru Takano; Maiya Hori; Takayuki Goto; Seiichi Uchida; Ryo Kurazume; Rin Ichiro Taniguchi

doi:10.5220/0006248806760683

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

Shigeru Takano, Maiya Hori, Takayuki Goto, Seiichi Uchida, Ryo Kurazume, Rin Ichiro Taniguchi

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

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
巻	2017-January
ISBN（電子版）	9789897582226
DOI	https://doi.org/10.5220/0006248806760683
出版ステータス	出版済み - 1月 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月 24 2017 → 2月 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
巻	2017-January

会議

会議	6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
国/地域	ポルトガル
City	Porto
Period	2/24/17 → 2/26/17

!!!All Science Journal Classification (ASJC) codes

コンピュータビジョンおよびパターン認識

UN SDG

この成果は、次の持続可能な開発目標に貢献しています

文献へのアクセス

10.5220/0006248806760683

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引用スタイル

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

Deep learning-based prediction method for people flows and their anomalies. / Takano, Shigeru; Hori, Maiya; Goto, Takayuki その他.
ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. ed. / Maria De De Marsico; Gabriella Sanniti di Baja; Ana Fred. Vol. 2017-January SciTePress, 2017. p. 676-683 (ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods; Vol. 2017-January).

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

Takano, S, Hori, M, Goto, T, Uchida, S , Kurazume, R & Taniguchi, RI 2017, Deep learning-based prediction method for people flows and their anomalies. in MD De Marsico, GS di Baja & A Fred (eds), ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. vol. 2017-January, ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, vol. 2017-January, SciTePress, pp. 676-683, 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017, Porto, ポルトガル, 2/24/17. https://doi.org/10.5220/0006248806760683

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

Takano, Shigeru ; Hori, Maiya ; Goto, Takayuki その他. / Deep learning-based prediction method for people flows and their anomalies. ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. editor / Maria De De Marsico ; Gabriella Sanniti di Baja ; Ana Fred. Vol. 2017-January SciTePress, 2017. pp. 676-683 (ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods).

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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.",

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AB - 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.

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

抄録

出版物シリーズ

会議

!!!All Science Journal Classification (ASJC) codes

UN SDG

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