TY - GEN
T1 - Deep learning-based prediction method for people flows and their anomalies
AU - Takano, Shigeru
AU - Hori, Maiya
AU - Goto, Takayuki
AU - Uchida, Seiichi
AU - Kurazume, Ryo
AU - Taniguchi, Rin Ichiro
N1 - Publisher Copyright:
© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85049419721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049419721&partnerID=8YFLogxK
U2 - 10.5220/0006248806760683
DO - 10.5220/0006248806760683
M3 - Conference contribution
AN - SCOPUS:85049419721
VL - 2017-January
T3 - ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
SP - 676
EP - 683
BT - ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
A2 - De Marsico, Maria De
A2 - di Baja, Gabriella Sanniti
A2 - Fred, Ana
PB - SciTePress
T2 - 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Y2 - 24 February 2017 through 26 February 2017
ER -
