Extracting latent behavior patterns of people from probe request data: A non-negative tensor factorization approach

Kaito Oka, Masaki Igarashi, Atsushi Shimada, Rin Ichiro Taniguchi

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

1 Citation (Scopus)

Abstract

Although people flow analysis is widely studied because of its importance, there are some difficulties with previous methods, such as the cost of sensors, person re-identification, and the spread of smartphone applications for collecting data. Today, Probe Request sensing for people flow analysis is gathering attention because it conquers many of the difficulties of previous methods. We propose a framework for Probe Request data analysis for extracting the latent behavior patterns of people. To make the extracted patterns understandable, we apply a Non-negative Tensor Factorization with a sparsity constraint and initialization with prior knowledge to the analysis. Experimental result showed that our framework helps the interpretation of Probe Request data.

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
Pages157-164
Number of pages8
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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