Authors:
Hiroki Mori
;
Takaomi Kanda
;
Dai Hirose
and
Minoru Asada
Affiliation:
Osaka University, Japan
Keyword(s):
4-Dimensional Pattern Recognition, Higher-order Local Auto-correlation, Point Cloud Time Series, Voxel Time Series, Tesseractic Pattern, IXMAS Dataset.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Theory and Methods
;
Video Analysis
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.