TY - GEN
T1 - Identifying motion capture tracking markers with self-organizing maps
AU - Weber, Matthias
AU - Amor, Heni Ben
AU - Alexander, Thomas
PY - 2008
Y1 - 2008
N2 - Motion Capture (MoCap) describes methods and technologies for the detection and measurement of human motion in all its intricacies. Most systems use markers to track points on a body. Especially with natural human motion data captured with passive systems (to not hinder the participant) deficiencies like low accuracy of tracked points or even occluded markers might occur. Additionally, such MoCap data is often unlabeled. In consequence, the system does not provide information about which body landmarks the points belong to. Self-organizing neural networks, especially selforganizing maps (SOMs), are capable of dealing with such problems. This work describes a method to model, initialize and train such SOMs to track and label potentially noisy motion capture data.
AB - Motion Capture (MoCap) describes methods and technologies for the detection and measurement of human motion in all its intricacies. Most systems use markers to track points on a body. Especially with natural human motion data captured with passive systems (to not hinder the participant) deficiencies like low accuracy of tracked points or even occluded markers might occur. Additionally, such MoCap data is often unlabeled. In consequence, the system does not provide information about which body landmarks the points belong to. Self-organizing neural networks, especially selforganizing maps (SOMs), are capable of dealing with such problems. This work describes a method to model, initialize and train such SOMs to track and label potentially noisy motion capture data.
KW - Connectionism and neural nets
KW - H. 1.2 [models and principles]: User/machine systems
KW - Human information processing; 1.2.6 [artificial intelligence]: Learning
UR - http://www.scopus.com/inward/record.url?scp=50249144686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50249144686&partnerID=8YFLogxK
U2 - 10.1109/VR.2008.4480809
DO - 10.1109/VR.2008.4480809
M3 - Conference contribution
AN - SCOPUS:50249144686
SN - 9781424419715
T3 - Proceedings - IEEE Virtual Reality
SP - 297
EP - 298
BT - IEEE Virtual Reality 2008, VR
T2 - IEEE Virtual Reality 2008, VR
Y2 - 8 March 2008 through 12 March 2008
ER -