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Efficient Motion-Searching Using the Joint Decomposition Approach

Published: 18 February 2017 Publication History

Abstract

A large amount of human action data can be captured in databases, with the rapid performance improvement and popularization of sensor devices. Although many methods of human motions search based on multidimensional time series from sensors have already been proposed, both the accuracy should be improved and the processing cost should be reduced. In this paper, the authors verify that the noise generated in a specific joint can be reduced by performing similarity calculation for each joint. The authors propose the Joint Decomposition approach and apply it to A-LTK (Approximation using Local features at Thinned-out Keypoints) to enable similarity calculation and weighting for each joint. The Joint Decomposition approach focuses on parameter of A-LTK and sets for each joint. Parameter represents the approximation level. The authors evaluate the Joint Decomposition approach along with existing methods. The evaluation result shows that the Joint Decomposition approach is better than other methods. From this result, it is found that noise generated in a specific joint can be reduced.

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ICCAE '17: Proceedings of the 9th International Conference on Computer and Automation Engineering
February 2017
365 pages
ISBN:9781450348096
DOI:10.1145/3057039
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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  • Macquarie U., Austarlia

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Association for Computing Machinery

New York, NY, United States

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Published: 18 February 2017

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Author Tags

  1. Approximation
  2. DTW
  3. Human behavior
  4. Social dance
  5. Time series data

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