Motion Retrieval Based on Kinetic Features in Large Motion Database

Considering the increasing collections of motion capture data, motion retrieval in large motion databases is gaining in importance. In this paper, we introduce kinetic interval features describing the movement trend of motions. In our approach, motion files are decomposed into kinetic intervals. For each joint in a kinetic interval, we define the kinetic interval features as the parameters of parametric arc equations computed by fitting joints trajectories. By extracting these features, we are able to lower the dimensionality and reconstruct the motions. Multilayer index tree is used to accelerate the searching process and a candidate list of motion data is generated for matching. To find both logically and numerically similar motions, we propose a two-level similarity matching based on kinetic interval features, which can also speed up the matching process. Experiments are performed on several variants of HDM05 and CMU motion databases proving that the approach can achieve accurate and fast motion retrieval in large motion databases.