A visual-numeric approach to clustering and anomaly detection for trajectory data
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- 作者：Dheeraj Kumar ; James C. Bezdek ; Sutharshan Rajasegarar…
- 关键词：Trajectory clustering ; Anomaly detection ; ClusiVAT hierarchical clustering ; MIT trajectory dataset
- 刊名：The Visual Computer
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：33
- 期：3
- 页码：265-281
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision;
- 出版者：Springer Berlin Heidelberg
- ISSN：1432-2315
- 卷排序：33
文摘
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.