Big trajectory data analysis for clustering and anomaly detection

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
出版社MVA Organization
ページ451-454
ページ数4
ISBN(印刷版)9784901122139
出版ステータスPublished - 2013
イベント13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan
継続期間: 2013 5 202013 5 23

出版物シリーズ

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

Conference

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

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

フィンガープリント

「Big trajectory data analysis for clustering and anomaly detection」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル