Human motion analysis; instrumental errors; Kalman filter; data fusion; stereophotogrammetry
Abstract :
[en] In motion capture systems, markers are often seen by multiple cameras. All cameras do not measure the position of the markers with the same reliability because of environmental factors such as the position of the marker in the field of view or the light intensity received by the cameras. Kalman filters offer a general framework to take the reliability of the various cameras into account and consequently improve the estimation of the marker position. The proposed process can be applied to both passive and active systems. Several reliability models of the cameras are compared for the Codamotion active system, which is considered as a specific illustration. The proposed method significantly reduces the noise in the signal, especially at long range distances. Therefore, it improves the confidence of the positions at the limits of the field of view.
Research center :
Laboratoire d'Analyse du Mouvement Humain (LAMH) - Université de Liège
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Schwartz, Cédric ; Université de Liège - ULiège > Département des sciences de la motricité > Kinésithérapie générale et réadaptation
Denoël, Vincent ; Université de Liège - ULiège > Département ArGEnCo > Analyse sous actions aléatoires en génie civil
Forthomme, Bénédicte ; Université de Liège - ULiège > Département des sciences de la motricité > Rééducation du membre supérieur
Croisier, Jean-Louis ; Université de Liège - ULiège > Département des sciences de la motricité > Kinésithérapie générale et réadaptation
Bruls, Olivier ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Laboratoire des Systèmes Multicorps et Mécatroniques
Language :
English
Title :
Merging multi-camera data to reduce motion analysis instrumental errors using Kalman filters
Publication date :
2015
Journal title :
Computer Methods in Biomechanics and Biomedical Engineering
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