Karadeniz, T.Maras, H.H.06.09. Yazılım Mühendisliği06. Mühendislik Fakültesi01. Çankaya Üniversitesi2025-11-062025-11-0620169781509016792https://doi.org/10.1109/SIU.2016.7496081https://hdl.handle.net/20.500.12416/15716In 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.trinfo:eu-repo/semantics/closedAccessClusteringDynamic Time WarpingTrajectory ClassificationConference Object10.1109/SIU.2016.74960812-s2.0-84982786596