Identifying Motion Capture Tracking Markers with Self-Organizing Maps

Abstract

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 self- organizing 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.