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The Visual Computer

, Volume 33, Issue 3, pp 265–281 | Cite as

A visual-numeric approach to clustering and anomaly detection for trajectory data

  • Dheeraj Kumar
  • James C. Bezdek
  • Sutharshan Rajasegarar
  • Christopher Leckie
  • Marimuthu Palaniswami
Original Article

Abstract

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.

Keywords

Trajectory clustering Anomaly detection ClusiVAT hierarchical clustering MIT trajectory dataset 

Notes

Acknowledgments

We acknowledge the support from the Australian Research Council (ARC) Linkage Project grant (LP120100529), the ARC Linkage Infrastructure, Equipment and Facilities scheme (LIEF) grant (LF120100129), the EU-FP7 SOCIOTAL grant and National ICT Australia (NICTA).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Dheeraj Kumar
    • 1
  • James C. Bezdek
    • 2
  • Sutharshan Rajasegarar
    • 2
    • 3
  • Christopher Leckie
    • 2
    • 3
  • Marimuthu Palaniswami
    • 1
  1. 1.Department of Electrical and Electronic EngineeringThe University of MelbourneMelbourneAustralia
  2. 2.Department of Computing and Information SystemsThe University of MelbourneMelbourneAustralia
  3. 3.National ICT AustraliaMelbourneAustralia

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