3D face tracking for infant monitoring using dense HOG and drift reduction

R.W.J.J. Saeijs, W.E. Tjon A Ten, P.H.N. de With

Research output: Contribution to conferencePaper

57 Downloads (Pure)

Abstract

This paper presents a new algorithm for 3D face tracking intended for clinical infant pain monitoring. The algorithm uses a cylinder head model and 3D head pose recovery by alignment of dynamically extracted templates based on dense-HOG features. Drift reduction is obtained from re-registration in combination with multi-pose state estimation by a square-root unscented Kalman filter. Results on videos of moving infants in hospital show good tracking for poses up to 50 degrees from upright-frontal, with mean eyelocation error relative to inter-ocular distance below 9%.
Original languageEnglish
Pages1-2
Publication statusPublished - 2016
EventThe Netherlands Conference on Computer Vision (NCCV 2016) - Lunteren, Netherlands
Duration: 12 Dec 201613 Dec 2016
http://http://www.nccv16.nl

Conference

ConferenceThe Netherlands Conference on Computer Vision (NCCV 2016)
Abbreviated titleNCCV 2016
Country/TerritoryNetherlands
CityLunteren
Period12/12/1613/12/16
Internet address

Keywords

  • face tracking
  • pain monitoring
  • cylinder head model
  • dense HOG

Fingerprint

Dive into the research topics of '3D face tracking for infant monitoring using dense HOG and drift reduction'. Together they form a unique fingerprint.

Cite this