Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Motion map: image-based retrieval and segmentation of motion data
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Indexing of variable length multi-attribute motion data
Proceedings of the 2nd ACM international workshop on Multimedia databases
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
An efficient search algorithm for motion data using weighted PCA
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Efficient human motion retrieval in large databases
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
Motion templates for automatic classification and retrieval of motion capture data
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)
Perceptually consistent example-based human motion retrieval
Proceedings of the 2009 symposium on Interactive 3D graphics and games
Content-based retrieval for human motion data
Journal of Visual Communication and Image Representation
Fast local and global similarity searches in large motion capture databases
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A real-time database architecture for motion capture data
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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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.