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Motion retrieval based on kinetic features in large motion database

Authors:

Gangyi DingAuthors Info & Claims

ICMI '12: Proceedings of the 14th ACM international conference on Multimodal interaction

Pages 209 - 216

https://doi.org/10.1145/2388676.2388718

Published: 22 October 2012 Publication History

Abstract

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.

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Cited By

Liu XHe GPeng SCheung YTang Y(2017)Efficient Human Motion Retrieval via Temporal Adjacent Bag of Words and Discriminative Neighborhood Preserving Dictionary LearningIEEE Transactions on Human-Machine Systems10.1109/THMS.2017.267595947:6(763-776)Online publication date: Dec-2017
https://doi.org/10.1109/THMS.2017.2675959
Jing-Fu Juang Teng W(2016)Utilizing motion data retrieval techniques for person identification2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)10.1109/ICKEA.2016.7803016(188-192)Online publication date: Sep-2016
https://doi.org/10.1109/ICKEA.2016.7803016
Pantuwong NTakahara KSugimoto M(2015)A rapid motion retrieval technique using simple and discrete representation of motion data2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE)10.1109/ICITEED.2015.7408915(70-75)Online publication date: Oct-2015
https://doi.org/10.1109/ICITEED.2015.7408915
Show More Cited By

Index Terms

Motion retrieval based on kinetic features in large motion database
1. Computing methodologies
  1. Computer graphics
    1. Animation
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information

Published In

cover image ACM Conferences

ICMI '12: Proceedings of the 14th ACM international conference on Multimodal interaction

October 2012

636 pages

ISBN:9781450314671

DOI:10.1145/2388676

General Chairs:
Louis-Philippe Morency
University of Southern California, USA
,
Dan Bohus
Microsoft Research, USA
,
Hamid Aghajan
Stanford University, USA
,
Program Chairs:
Justine Cassell
Carnegie Mellon University, USA
,
Anton Nijholt
University of Twente, Netherlands
,
Julien Epps
The University of New South Wales, Australia

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2012

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ICMI '12

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SIGCHI

ICMI '12: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 22 - 26, 2012

California, Santa Monica, USA

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Liu XHe GPeng SCheung YTang Y(2017)Efficient Human Motion Retrieval via Temporal Adjacent Bag of Words and Discriminative Neighborhood Preserving Dictionary LearningIEEE Transactions on Human-Machine Systems10.1109/THMS.2017.267595947:6(763-776)Online publication date: Dec-2017
https://doi.org/10.1109/THMS.2017.2675959
Jing-Fu Juang Teng W(2016)Utilizing motion data retrieval techniques for person identification2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)10.1109/ICKEA.2016.7803016(188-192)Online publication date: Sep-2016
https://doi.org/10.1109/ICKEA.2016.7803016
Pantuwong NTakahara KSugimoto M(2015)A rapid motion retrieval technique using simple and discrete representation of motion data2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE)10.1109/ICITEED.2015.7408915(70-75)Online publication date: Oct-2015
https://doi.org/10.1109/ICITEED.2015.7408915
Xiao JTang ZFeng YXiao Z(2015)Sketch-based human motion retrieval via selected 2D geometric posture descriptorSignal Processing10.1016/j.sigpro.2015.01.004113(1-8)Online publication date: Aug-2015
https://doi.org/10.1016/j.sigpro.2015.01.004
Da-Cheng Yu Teng W(2014)Indexing and retrieval of human motion data based on a growing self-organizing map2014 International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2014.7058053(66-71)Online publication date: Oct-2014
https://doi.org/10.1109/DSAA.2014.7058053
Fotiadou ENikolaidis N(2014)Activity-based methods for person recognition in motion capture sequencesPattern Recognition Letters10.1016/j.patrec.2014.06.00549:C(48-54)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1016/j.patrec.2014.06.005
Zhou LLu ZLeung HShang L(2014)Spatial temporal pyramid matching using temporal sparse representation for human motion retrievalThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-014-0957-y30:6-8(845-854)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s00371-014-0957-y
Tang ZXiao JFeng YYang XZhang J(2014)Human motion retrieval based on freehand sketchComputer Animation and Virtual Worlds10.1002/cav.160225:3-4(273-281)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1002/cav.1602
Sedmidubsky JValcik JZezula P(2013)A Key-Pose Similarity Algorithm for Motion Data RetrievalAdvanced Concepts for Intelligent Vision Systems10.1007/978-3-319-02895-8_60(669-681)Online publication date: 2013
https://doi.org/10.1007/978-3-319-02895-8_60
Qi TFeng YXiao JZhuang YYang XZhang J(2013)A semantic feature for human motion retrievalComputer Animation and Virtual Worlds10.1002/cav.150524:3-4(399-407)Online publication date: 15-May-2013
https://doi.org/10.1002/cav.1505

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