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

Hiroki Mori, Takaomi Kanda, Dai Hirose, Minoru Asada

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
出版者SciTePress
ページ223-231
ページ数9
1
ISBN(電子版)9789897580765
出版物ステータスPublished - 2015
外部発表Yes
イベント4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015 - Lisbon, Portugal
継続期間: 2015 1 102015 1 12

Other

Other4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015
Portugal
Lisbon
期間15/1/1015/1/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • これを引用

    Mori, H., Kanda, T., Hirose, D., & Asada, M. (2015). 3-Dimensional motion recognition by 4-dimensional Higher-Order Local Auto-Correlation. : ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings (巻 1, pp. 223-231). SciTePress.