3-Dimensional motion recognition by 4-dimensional Higher-Order Local Auto-Correlation

Hiroki Mori, Takaomi Kanda, Dai Hirose, Minoru Asada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a 4-Dimensional Higher-order Local Auto-Correlation (4D HLAC). The method aims to extract the features of a 3D time series, which is regarded as a 4D static pattern. This is an orthodox extension of the original HLAC, which represents correlations among local values in 2D images and can effectively summarize motion in 3D space. To recognize motion in the real world, a recognition system should exploit motion information from the real-world structure. The 4D HLAC feature vector is expected to capture representations for general 3D motion recognition, because the original HLAC performed very well in image recognition tasks. Based on experimental results showing high recognition performance and low computational cost, we conclude that our method has a strong advantage for 3D time series recognition, even in practical situations.

Original languageEnglish
Title of host publicationICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
PublisherSciTePress
Pages223-231
Number of pages9
Volume1
ISBN (Electronic)9789897580765
Publication statusPublished - 2015
Externally publishedYes
Event4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015 - Lisbon, Portugal
Duration: 2015 Jan 102015 Jan 12

Other

Other4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015
CountryPortugal
CityLisbon
Period15/1/1015/1/12

Keywords

  • 4-dimensional pattern recognition
  • Higher-Order Local Auto-Correlation
  • IXMAS Dataset
  • Point cloud time series
  • Tesseractic pattern
  • Voxel time series

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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