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

• Published in: The Visual computer Vol. 33; no. 3; pp. 265 - 281
• Main Authors: Kumar, Dheeraj, Bezdek, James C., Rajasegarar, Sutharshan, Leckie, Christopher, Palaniswami, Marimuthu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2017; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Artificial Intelligence; Cameras; Cluster analysis; Clustering; Computer Graphics; Computer Science; Datasets; Distance learning; Explicit knowledge; Graph theory; Image Processing and Computer Vision; Original Article; Pedestrians; Support vector machines; Surveillance; Synthetic data; Trajectory analysis; Trajectory clustering; ClusiVAT hierarchical clustering; Anomaly detection; MIT trajectory dataset
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-015-1192-x

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.