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dc.contributor.authorÖǧrenci, Arif Selçuk
dc.date.accessioned2020-12-20T20:17:01Z
dc.date.available2020-12-20T20:17:01Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3616
dc.description.abstractAnalysis of the walking trajectory and the detection of anomalies in this trajectory, provide important benefits in the fields of health and security. In this work, two methods to detect anomalies in trajectories, are compared. Firstly, an unsupervised method is used where the conformance among trajectories are taken into consideration. Trajectories that deviate from others are qualified as anomalies. Secondly, the points in the trajectories are considered as a time series. Artifical neural networks performing supervised learning based on the backpropagation algorithm are used. The results are compared, and the points to be enhanced are highlighted.en_US
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.subjectAnomaly detectionen_US
dc.subjectTrajectoryen_US
dc.subjectConformal predictionen_US
dc.subjectArtifical neural networksen_US
dc.titleAnomaly Detection in Walking Trajectoryen_US
dc.typeProceedings Paperen_US
dc.relation.journal2018 26th Signal Processing And Communications Applications Conference (Siu)en_US
dc.identifier.wosWOS:000511448500650en_US
dc.contributor.khasauthorÖǧrenci, Arif Selçuken_US


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