Online Trajectory Segmentation and Summary With Applications to Visualization and Retrieval

TL;DR

This paper introduces a novel online algorithm for trajectory segmentation and summarization based on point density, facilitating visualization and efficient querying of large trajectory datasets.

Contribution

The paper presents a new online segmentation method leveraging point density and natural activity patterns, enabling scalable visualization and retrieval in large datasets.

Findings

Effective trajectory segmentation based on point density

Enhanced visualization of large trajectory datasets

Efficient querying enabled by trajectory summaries

Abstract

Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of locomotive and local activity. We show an application to visualization of trajectory datasets, and discuss the use of the summary as an index allowing efficient queries which are otherwise impossible or computationally expensive, over very large datasets.