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March 2014 Statistical analysis of trajectories on Riemannian manifolds: Bird migration, hurricane tracking and video surveillance
Jingyong Su, Sebastian Kurtek, Eric Klassen, Anuj Srivastava
Ann. Appl. Stat. 8(1): 530-552 (March 2014). DOI: 10.1214/13-AOAS701

Abstract

We consider the statistical analysis of trajectories on Riemannian manifolds that are observed under arbitrary temporal evolutions. Past methods rely on cross-sectional analysis, with the given temporal registration, and consequently may lose the mean structure and artificially inflate observed variances. We introduce a quantity that provides both a cost function for temporal registration and a proper distance for comparison of trajectories. This distance is used to define statistical summaries, such as sample means and covariances, of synchronized trajectories and “Gaussian-type” models to capture their variability at discrete times. It is invariant to identical time-warpings (or temporal reparameterizations) of trajectories. This is based on a novel mathematical representation of trajectories, termed transported square-root vector field (TSRVF), and the $\mathbb{L}^{2}$ norm on the space of TSRVFs. We illustrate this framework using three representative manifolds—$\mathbb{S}^{2}$, $\mathrm{SE}(2)$ and shape space of planar contours—involving both simulated and real data. In particular, we demonstrate: (1) improvements in mean structures and significant reductions in cross-sectional variances using real data sets, (2) statistical modeling for capturing variability in aligned trajectories, and (3) evaluating random trajectories under these models. Experimental results concern bird migration, hurricane tracking and video surveillance.

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Jingyong Su. Sebastian Kurtek. Eric Klassen. Anuj Srivastava. "Statistical analysis of trajectories on Riemannian manifolds: Bird migration, hurricane tracking and video surveillance." Ann. Appl. Stat. 8 (1) 530 - 552, March 2014. https://doi.org/10.1214/13-AOAS701

Information

Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302246
MathSciNet: MR3192001
Digital Object Identifier: 10.1214/13-AOAS701

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 1 • March 2014
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