Covariance Features for Trajectory Analysis
No Thumbnail Available
Date
2016
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Open Access Color
GOLD
Green Open Access
Yes
OpenAIRE Downloads
6
OpenAIRE Views
1
Publicly Funded
No
BIP! Indicators
Impulse
Average
Influence
Average
Popularity
Average
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.
Description
Keywords
Clustering, Dynamic Time Warping, Trajectory Classification, covariance matrices, sign language, time series analysis., data mining, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Turkish CoHE Thesis Center URL
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
WoS Q
Q4
Scopus Q
Q3
OpenCitations Citation Count
N/A
Source
-- 24th Signal Processing and Communication Application Conference, SIU 2016 -- Zonguldak -- 122605
Volume
24
Issue
Start Page
1681
End Page
1684
Collections
PlumX Metrics
Citations
Scopus : 0
Page Views
2
checked on Feb 01, 2026
Google Scholar™
