Trajectory analysis via a geometric feature space approach

Journal Article · Mon Oct 05 00:00:00 EDT 2015 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11287· OSTI ID:1262244

Rintoul, Mark D. ^[1]; Wilson, Andrew T. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1262244
• Report Number(s): SAND-2016-3716J; 643724
• Journal Information: Statistical Analysis and Data Mining, Vol. 8, Issue 5-6; ISSN 1932-1864
• Publisher: Wiley
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
algorithms
measurement
verification
trajectory
flight
feature vectors
clustering