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

Version 2 2024-06-04, 06:14
Version 1 2016-03-14, 08:41
journal contribution
posted on 2024-06-04, 06:14 authored by D Kumar, JC Bezdek, Sutharshan RajasegararSutharshan Rajasegarar, C Leckie, M Palaniswami
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.

History

Journal

Visual computer

Volume

33

Pagination

265-281

Location

Berlin, Germany

ISSN

0178-2789

eISSN

1432-2315

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2015, Springer

Issue

3

Publisher

Springer