Statistical analysis of trajectories on riemannian manifolds: Bird migration, hurricane tracking and video surveillance

Jingyong Su, Sebastian Kurtek, Eric Klassen, Anuj Srivastava

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


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 L2 norm on the space of TSRVFs. We illustrate this framework using three representative manifolds-S2, 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.

Original languageEnglish
Pages (from-to)530-552
Number of pages23
JournalAnnals of Applied Statistics
Issue number1
StatePublished - Mar 2014


  • Parallel transport
  • Rate invariant
  • Riemannian manifold
  • Temporal trajectory
  • Time warping
  • Variance reduction


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