A Human Motion Database: The Cognitive And Parametric Sampling Of Human Motion

Abstract

Motion databases have a strong potential to guide progress in the field of machine recognition and motion-based animation. Existing databases either have a very loose structure that do not sample the domain according to any controlled methodology or too few action samples which limits their potential to quantitatively evaluate the performance of motion-based techniques. The controlled sampling of the motor domain in the database may lead investigators to identify the fundamental difficulties of motion cognition problems and allow the addressing of these issues in a more objective way. In this thesis, we describe the construction of our Human Motion Database using controlled sampling methods (parametric and cognitive sampling) to obtain the structure necessary for the quantitative evaluation of several motion-based research problems. The Human Motion Database is organized into several components: the praxicon dataset, the cross-validation dataset, the generalization dataset, the compositionality dataset, and the interaction dataset. The main contributions of this thesis include (1) a survey of human motion databases describing data sources related to motion synthesis and analysis problems, (2) a sampling methodology that takes advantage of a systematic controlled capture, denoted as cognitive sampling and parametric sampling, (3) a novel structured motion database organized into several datasets addressing a number of aspects in the motion domain, (4) a study of the design decisions needed to build a custom skeleton to generate joint angle data from marker data, and (5) a study of the motion capture technologies and the general optical motion capture workflow including capturing and post processing data.