Big trajectory data analysis for clustering and anomaly detection

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

Big trajectory data analysis for clustering and anomaly detection

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

Department of Electronics and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
Publisher	MVA Organization
Pages	451-454
Number of pages	4
ISBN (Print)	9784901122139
Publication status	Published - 2013
Event	13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan Duration: 2013 May 20 → 2013 May 23

Publication series

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

Conference

Conference	13th IAPR International Conference on Machine Vision Applications, MVA 2013
Country/Territory	Japan
City	Kyoto
Period	13/5/20 → 13/5/23

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Kataoka, H., Aoki, Y., Iwata, K., Satoh, Y., Yoda, I., & Onishi, M. (2013). Big trajectory data analysis for clustering and anomaly detection. In Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013 (pp. 451-454). (Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013). MVA Organization.

Big trajectory data analysis for clustering and anomaly detection. / Kataoka, Hirokatsu; Aoki, Yoshimitsu; Iwata, Kenji et al.

Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013. MVA Organization, 2013. p. 451-454 (Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kataoka, H, Aoki, Y, Iwata, K, Satoh, Y, Yoda, I & Onishi, M 2013, Big trajectory data analysis for clustering and anomaly detection. in Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013. Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013, MVA Organization, pp. 451-454, 13th IAPR International Conference on Machine Vision Applications, MVA 2013, Kyoto, Japan, 13/5/20.

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author = "Hirokatsu Kataoka and Yoshimitsu Aoki and Kenji Iwata and Yutaka Satoh and Ikushi Yoda and Masaki Onishi",

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AU - Iwata, Kenji

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AU - Yoda, Ikushi

AU - Onishi, Masaki

PY - 2013

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AB - 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.

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M3 - Conference contribution

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

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Big trajectory data analysis for clustering and anomaly detection

Abstract

Publication series

Conference

ASJC Scopus subject areas

UN SDGs

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