Trajectory Clustering in an Intersection by GDTW

Clustering by intersection-merging

We propose Interse tion-Merging (IM), a wrapper algorithm for model-based lustering. The algorithm takes a set of lusterings obtained e.g. by EM, breaks down the lusterings into sub lusters via an interse tion step, and then agglomerates them via a merging step. We introdu e two versions of merging: greedy (standard IM) and by simulated annealing (IMSA). Experiments on several data sets show th...

Shape-based Trajectory Clustering

Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning and sports analysis, and has generated great interest and investigation. The purpose of this work is to propose and test new methods for trajectory clustering, based on shape, rather than spatial position, as is the case with previous methods. The...

Trajectory Clustering and Coastal Surveillance

In this paper we explore trajectory clustering as a means for representing the normal behavior of vessels in a coastal surveillance scenario. Trajectory clustering however suffers from some drawbacks in this type of setting and we therefore propose a new approach, spline-based clustering, with a potential for solving the task of representing the normal course of events.

Sampling-based Sub-Trajectory Clustering

In-DBMS Sampling-based Sub-trajectory Clustering

In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajectories according to a local density criterion and trajectory similarity criteria, followed by a sampling-and-clustering phase that selects the most...