Big trajectory data analysis for clustering and anomaly detection

Hirokatsu Kataoka, Yoshimitsu Aoki, Kenji Iwata, Yutaka Satoh, Ikushi Yoda, Masaki Onishi

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

Abstract

We’ve been developing a sensor that can acquire positional data. Recently, a position-based big data creation is easy task and trajectory analysis is the highest priority for ”position-based service”. Traffic congestion, marketing mining, and pattern analysis are the one of the examples in trajectory analysis field. In this paper, we propose the trajectory analysis approach for clustering and anomaly detection by using big trajectory data. To execute clustering, we understand an environment in front of the camera and set a cluster route from trajectory map. The experiment shows that the proposed method understands environment and performs clustering. Moreover, the approach classifies anomalies from big data.

Original languageEnglish
Title of host publicationProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
PublisherMVA Organization
Pages451-454
Number of pages4
ISBN (Print)9784901122139
Publication statusPublished - 2013
Event13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan
Duration: 2013 May 202013 May 23

Publication series

NameProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013

Conference

Conference13th IAPR International Conference on Machine Vision Applications, MVA 2013
CountryJapan
CityKyoto
Period13/5/2013/5/23

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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