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Title: PANTHER. Trajectory Analysis

Abstract

We want to organize a body of trajectories in order to identify, search for, classify and predict behavior among objects such as aircraft and ships. Existing compari- son functions such as the Fr'echet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as total distance traveled and distance be- tween start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally, these features can generally be mapped easily to behaviors of interest to humans that are searching large databases. Most of these geometric features are invariant under rigid transformation. We demonstrate the use of different subsets of these features to iden- tify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories, predict destination and apply unsupervised machine learning algorithms.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1221864
Report Number(s):
SAND2015-8120
604041
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Rintoul, Mark Daniel, Wilson, Andrew T., Valicka, Christopher G., Kegelmeyer, W. Philip, Shead, Timothy M., Newton, Benjamin D., and Czuchlewski, Kristina Rodriguez. PANTHER. Trajectory Analysis. United States: N. p., 2015. Web. doi:10.2172/1221864.
Rintoul, Mark Daniel, Wilson, Andrew T., Valicka, Christopher G., Kegelmeyer, W. Philip, Shead, Timothy M., Newton, Benjamin D., & Czuchlewski, Kristina Rodriguez. PANTHER. Trajectory Analysis. United States. doi:10.2172/1221864.
Rintoul, Mark Daniel, Wilson, Andrew T., Valicka, Christopher G., Kegelmeyer, W. Philip, Shead, Timothy M., Newton, Benjamin D., and Czuchlewski, Kristina Rodriguez. Tue . "PANTHER. Trajectory Analysis". United States. doi:10.2172/1221864. https://www.osti.gov/servlets/purl/1221864.
@article{osti_1221864,
title = {PANTHER. Trajectory Analysis},
author = {Rintoul, Mark Daniel and Wilson, Andrew T. and Valicka, Christopher G. and Kegelmeyer, W. Philip and Shead, Timothy M. and Newton, Benjamin D. and Czuchlewski, Kristina Rodriguez},
abstractNote = {We want to organize a body of trajectories in order to identify, search for, classify and predict behavior among objects such as aircraft and ships. Existing compari- son functions such as the Fr'echet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as total distance traveled and distance be- tween start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally, these features can generally be mapped easily to behaviors of interest to humans that are searching large databases. Most of these geometric features are invariant under rigid transformation. We demonstrate the use of different subsets of these features to iden- tify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories, predict destination and apply unsupervised machine learning algorithms.},
doi = {10.2172/1221864},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 01 00:00:00 EDT 2015},
month = {Tue Sep 01 00:00:00 EDT 2015}
}

Technical Report:

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  • This document describes the PANTHER R&D Application, a proof-of-concept user interface application developed under the PANTHER Grand Challenge LDRD. The purpose of the application is to explore interaction models for graph analytics, drive algorithmic improvements from an end-user point of view, and support demonstration of PANTHER technologies to potential customers. The R&D Application implements a graph-centric interaction model that exposes analysts to the algorithms contained within the GeoGraphy graph analytics library. Users define geospatial-temporal semantic graph queries by constructing search templates based on nodes, edges, and the constraints among them. Users then analyze the results of the queries using bothmore » geo-spatial and temporal visualizations. Development of this application has made user experience an explicit driver for project and algorithmic level decisions that will affect how analysts one day make use of PANTHER technologies.« less