Efficient human motion retrieval in large databases

This paper provides methods for identifying visually and numerically similar motions in large motion capture databases given a query of motion segment. Large human motion databases contain variants of natural motions that are valuable for animation generation and synthesis. But retrieving visually similar motions is still a difficult and time-consuming problem. We propose an efficient geometric feature based indexing strategy that represents the motions compactly through apreprocessing. This representation scales down the range of searching the database. Motions in this range are possible candidates of the final matches. For detailed comparisons between the query and the candidates, we propose an algorithm that compares the motions' curves using an efficient motion curve matching algorithm. Our methods can apply to large human motion databases and achieve high performance and accuracy compared with previous work.

[1]  Christos Faloutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[2]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[3]  Bin Ma,et al.  PatternHunter: faster and more sensitive homology search , 2002, Bioinform..

[4]  Okan Arikan,et al.  Interactive motion generation from examples , 2002, ACM Trans. Graph..

[5]  Daniel G. Brown,et al.  Vector seeds: An extension to spaced seeds , 2005, J. Comput. Syst. Sci..

[6]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[7]  Jessica K. Hodgins,et al.  Interactive control of avatars animated with human motion data , 2002, SIGGRAPH.

[8]  Bobby Bodenheimer,et al.  An evaluation of a cost metric for selecting transitions between motion segments , 2003, SCA '03.

[9]  Ian H. Witten,et al.  Managing gigabytes , 1994 .

[10]  Dimitrios Gunopulos,et al.  Indexing multi-dimensional time-series with support for multiple distance measures , 2003, KDD '03.

[11]  Michael Gleicher,et al.  Automated extraction and parameterization of motions in large data sets , 2004, SIGGRAPH 2004.

[12]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[13]  Frank Kurth,et al.  A unified approach to content-based and fault-tolerant music recognition , 2004, IEEE Transactions on Multimedia.

[14]  Meinard Müller,et al.  Efficient content-based retrieval of motion capture data , 2005, SIGGRAPH '05.

[15]  Feng Liu,et al.  3D motion retrieval with motion index tree , 2003, Comput. Vis. Image Underst..

[16]  Lucas Kovar,et al.  Automated extraction and parameterization of motions in large data sets , 2004, ACM Trans. Graph..

[17]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.