TY - GEN
T1 - 3-Dimensional motion recognition by 4-dimensional Higher-Order Local Auto-Correlation
AU - Mori, Hiroki
AU - Kanda, Takaomi
AU - Hirose, Dai
AU - Asada, Minoru
N1 - Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - 4-dimensional pattern recognition
KW - Higher-Order Local Auto-Correlation
KW - IXMAS Dataset
KW - Point cloud time series
KW - Tesseractic pattern
KW - Voxel time series
UR - http://www.scopus.com/inward/record.url?scp=84938882272&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84938882272
T3 - ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
SP - 223
EP - 231
BT - ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
A2 - De Marsico, Maria
A2 - Fred, Ana
A2 - Figueiredo, Mario
PB - SciTePress
T2 - 4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015
Y2 - 10 January 2015 through 12 January 2015
ER -