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Proceedings Paper

Dense-HOG-based drift-reduced 3D face tracking for infant pain monitoring
Author(s): Ronald W.J.J. Saeijs; Walther E. Tjon A Ten; Peter H. N. de With
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Paper 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. The algorithm includes extensions for drift reduction, using re-registration in combination with multi-pose state estimation by means of a square-root unscented Kalman filter. The paper reports experimental results on videos of moving infants in hospital who are relaxed or in pain. Results show good tracking behavior for poses up to 50 degrees from upright-frontal. In terms of eye location error relative to inter-ocular distance, the mean tracking error is below 9%.

Paper Details

Date Published: 17 March 2017
PDF: 6 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411U (17 March 2017); doi: 10.1117/12.2268522
Show Author Affiliations
Ronald W.J.J. Saeijs, Technische Univ. Eindhoven (Netherlands)
Walther E. Tjon A Ten, Maxima Medical Ctr. Veldhoven (Netherlands)
Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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