Automated tracking of multiple body parts in video recordings of neonatal seizures

This paper presents an automated procedure for tracking multiple body parts in video recordings of neonatal seizures. This procedure detects motion by relying on optical flow computation and then tracks one or more body parts by employing predictive block matching. Predictive block matching estimate...

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Bibliographic Details
Published in2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821) pp. 312 - 315 Vol. 1
Main Authors Sami, A., Karayiannis, N.B., Frost, J.D.Jr, Wise, M.S., Mizrahi, E.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Adaptive filters
Image motion analysis
Kalman filters
Motion detection
Optical computing
Optical filters
Optical recording
Pediatrics
Tracking
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Summary:This paper presents an automated procedure for tracking multiple body parts in video recordings of neonatal seizures. This procedure detects motion by relying on optical flow computation and then tracks one or more body parts by employing predictive block matching. Predictive block matching estimates the displacement of a feature between two successive frames by minimizing an error function defined in terms of the feature intensities at these frames. Displacement estimation is followed by adaptive block matching based on Kalman filtering. The reliability of the proposed automated tracking procedure is illustrated by its application in the extraction of temporal motor activity signals from video recordings of neonatal seizures.
ISBN:0780383885
9780780383883
DOI:10.1109/ISBI.2004.1398537