Trajectory analysis via a geometric feature space approach
Abstract
This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet 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 the total distance traveled and the distance between 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 who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1262244
- Report Number(s):
- SAND-2016-3716J
Journal ID: ISSN 1932-1864; 643724
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Statistical Analysis and Data Mining
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 5-6; Journal ID: ISSN 1932-1864
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; algorithms; measurement; verification; trajectory; flight; feature vectors; clustering
Citation Formats
Rintoul, Mark D., and Wilson, Andrew T. Trajectory analysis via a geometric feature space approach. United States: N. p., 2015.
Web. doi:10.1002/sam.11287.
Rintoul, Mark D., & Wilson, Andrew T. Trajectory analysis via a geometric feature space approach. United States. https://doi.org/10.1002/sam.11287
Rintoul, Mark D., and Wilson, Andrew T. Mon .
"Trajectory analysis via a geometric feature space approach". United States. https://doi.org/10.1002/sam.11287. https://www.osti.gov/servlets/purl/1262244.
@article{osti_1262244,
title = {Trajectory analysis via a geometric feature space approach},
author = {Rintoul, Mark D. and Wilson, Andrew T.},
abstractNote = {This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet 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 the total distance traveled and the distance between 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 who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.},
doi = {10.1002/sam.11287},
journal = {Statistical Analysis and Data Mining},
number = 5-6,
volume = 8,
place = {United States},
year = {Mon Oct 05 00:00:00 EDT 2015},
month = {Mon Oct 05 00:00:00 EDT 2015}
}
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