dc.contributor.author | Öğrenci, Arif Selçuk | |
dc.date.accessioned | 2019-06-28T11:11:19Z | |
dc.date.available | 2019-06-28T11:11:19Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9781538615010 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12469/1538 | |
dc.identifier.uri | https://doi.org/10.1109/SIU.2018.8404797 | |
dc.description.abstract | Analysis 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.iso | tur | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Anomaly detection | en_US |
dc.subject | Artifical neural networks | en_US |
dc.subject | Conformal prediction | en_US |
dc.subject | Trajectory | en_US |
dc.title | Anomaly detection in walking trajectory [Yürüyüş yörüngesinde anormallik algılama] | en_US |
dc.type | conferenceObject | en_US |
dc.identifier.startpage | 1 | en_US |
dc.identifier.endpage | 4 | |
dc.relation.journal | 26th Signal Processing and Communications Applications Conference (SIU) | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.doi | 10.1109/SIU.2018.8404797 | en_US |
dc.identifier.scopus | 2-s2.0-85050824546 | en_US |
dc.institutionauthor | Öğrenci, Arif Selçuk | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |