Estimating missing marker positions using low dimensional Kalman smoothing

Michael Burke, J. Lasenby

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Motion capture is frequently used for studies in biomechanics, and has proved particularly useful in understanding human motion. Unfortunately, motion capture approaches often fail when markers are occluded or missing and a mechanism by which the position of missing markers can be estimated is highly desirable. Of particular interest is the problem of estimating missing marker positions when no prior knowledge of marker placement is known. Existing approaches to marker completion in this scenario can be broadly divided into tracking approaches using dynamical modelling, and low rank matrix completion. This paper shows that these approaches can be combined to provide a marker completion algorithm that not only outperforms its respective components, but also solves the problem of incremental position error typically associated with tracking approaches.
Original languageEnglish
Pages (from-to)1854-1858
Number of pages5
JournalJournal of Biomechanics
Issue number9
Early online date28 Apr 2016
Publication statusPublished - 14 Jun 2016


Dive into the research topics of 'Estimating missing marker positions using low dimensional Kalman smoothing'. Together they form a unique fingerprint.

Cite this