A Full-Body Gesture Database for Automatic Gesture Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Model based full body human motion reconstruction from video data
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
ChAirGest: a challenge for multimodal mid-air gesture recognition for close HCI
Proceedings of the 15th ACM on International conference on multimodal interaction
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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 does not sample the domain according to any controlled methodology or too few action samples which limit 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 paper, 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 paper 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, and (3) a novel structured motion database organized into several datasets addressing a number of aspects in the motion domain.