Covariance Features for Trajectory Analysis
| dc.contributor.author | Karadeniz, Talha | |
| dc.contributor.author | Maras, Hakan Hadi | |
| dc.date.accessioned | 2025-05-13T13:32:57Z | |
| dc.date.accessioned | 2025-09-18T14:10:45Z | |
| dc.date.available | 2025-05-13T13:32:57Z | |
| dc.date.available | 2025-09-18T14:10:45Z | |
| dc.date.issued | 2018 | |
| dc.description | Maras, Hadi Hakan/0000-0001-5117-3938 | en_US |
| dc.description.abstract | In this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series. | en_US |
| dc.description.sponsorship | TUBITAK [113S094]; TUBITAK | en_US |
| dc.description.sponsorship | This exploration is conducted for the Surgical Navigation Project (CAN) which is supported by TUBITAK (113S094). The engineering team would like to thank TUBITAK support for realizing this study. | en_US |
| dc.identifier.doi | 10.5755/j01.eie.24.3.15290 | |
| dc.identifier.issn | 1392-1215 | |
| dc.identifier.issn | 2029-5731 | |
| dc.identifier.scopus | 2-s2.0-85049809207 | |
| dc.identifier.uri | https://doi.org/10.5755/j01.eie.24.3.15290 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12416/13773 | |
| dc.language.iso | en | en_US |
| dc.publisher | Kaunas Univ Technology | en_US |
| dc.relation.ispartof | Elektronika ir Elektrotechnika | |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Covariance Matrices | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Sign Language | en_US |
| dc.subject | Time Series Analysis | en_US |
| dc.title | Covariance Features for Trajectory Analysis | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.id | Maras, Hadi Hakan/0000-0001-5117-3938 | |
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| gdc.author.wosid | Maras, Hadi Hakan/G-1236-2017 | |
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| gdc.description.department | Çankaya University | en_US |
| gdc.description.departmenttemp | [Karadeniz, Talha; Maras, Hakan Hadi] Cankaya Univ, Dept Comp Engn, Ankara, Turkey | en_US |
| gdc.description.endpage | 81 | en_US |
| gdc.description.issue | 3 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q3 | |
| gdc.description.startpage | 78 | en_US |
| gdc.description.volume | 24 | en_US |
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| gdc.oaire.keywords | covariance matrices | |
| gdc.oaire.keywords | sign language | |
| gdc.oaire.keywords | time series analysis. | |
| gdc.oaire.keywords | data mining | |
| gdc.oaire.keywords | Electrical engineering. Electronics. Nuclear engineering | |
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| gdc.virtual.author | Karadeniz, Talha | |
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