Covariance Features for Trajectory Analysis

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GOLD

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Yes

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Abstract

In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results. © 2017 Elsevier B.V., All rights reserved.

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Keywords

Clustering, Dynamic Time Warping, Trajectory Classification, covariance matrices, sign language, time series analysis., data mining, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Fields of Science

0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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Volume

24

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1681

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1684
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Scopus : 0

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2

checked on May 30, 2026

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