2-B hareket kestirimi ile baş dönüş hareketinin siniflandirilmasi

Inci M. Baytas, Bilge Gunsel

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

Özet

This work aims to classify the changes in head pose of a user sitting in front of a screen by using the estimated head rotation. Considered classes include ∓15, ∓30, ve ∓ 45 degree pan, tilt and combinations of these poses. SIFT flow algorithm is used for motion estimation. Two dimensional feature vectors are extracted by calculating the magnitude and the angle of the flow vectors. Classification has been performed by Support Vector Machine and Naive Bayesian classifiers. Test results reported on Pointing'04 database demonstrate that SIFT flow vectors enable us classifying head rotation with high accuracy, when the desired resolution is not in the order of degrees.

Tercüme edilen katkı başlığıHead motion classification with 2D motion estimation
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
YayınlayanIEEE Computer Society
Sayfalar325-328
Sayfa sayısı4
ISBN (Basılı)9781479948741
DOI'lar
Yayın durumuYayınlandı - 2014
Etkinlik2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Trabzon, Turkey
Süre: 23 Nis 201425 Nis 2014

Yayın serisi

Adı2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

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???event.eventtypes.event.conference???2014 22nd Signal Processing and Communications Applications Conference, SIU 2014
Ülke/BölgeTurkey
ŞehirTrabzon
Periyot23/04/1425/04/14

Keywords

  • head pose classification
  • optical flow
  • SIFT

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