On Multiscale Self-Similarities Description for Effective Three-Dimensional/Six-Dimensional Motion Trajectory Recognition
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 8031346 |
Pages (from-to) | 3017-3026 |
Journal / Publication | IEEE Transactions on Industrial Informatics |
Volume | 13 |
Issue number | 6 |
Online published | 11 Sep 2017 |
Publication status | Published - Dec 2017 |
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Abstract
Motion trajectories provide compact informative clues in characterizing motion behaviors of human bodies, robots, and moving objects. This paper devises an
invariant and unified descriptor for three-dimensional/sixdimensional
(3-D/6-D) motion trajectories recognition by exploring the latent motion patterns in the multiscale selfsimilarity matrices (MSM) within a motion trajectory and its components. The MSM approach transforms a motion trajectory in Euclidean space into a set of similarity matrices and exhibits strong invariances, in which each matrix can be regarded as a grayscale image. Next, the histograms of oriented gradients features extracted from the MSM representation are concatenated as the final trajectory descriptor. In addition, an improved kernel MSM is raised by calculating the pairwise kernel distances. Finally, extensive 3-D/6-D motion trajectory recognition experiments on three public datasets with a linear support vector machine classifier are conducted to verify the effectiveness and efficiency of the proposed approach.
invariant and unified descriptor for three-dimensional/sixdimensional
(3-D/6-D) motion trajectories recognition by exploring the latent motion patterns in the multiscale selfsimilarity matrices (MSM) within a motion trajectory and its components. The MSM approach transforms a motion trajectory in Euclidean space into a set of similarity matrices and exhibits strong invariances, in which each matrix can be regarded as a grayscale image. Next, the histograms of oriented gradients features extracted from the MSM representation are concatenated as the final trajectory descriptor. In addition, an improved kernel MSM is raised by calculating the pairwise kernel distances. Finally, extensive 3-D/6-D motion trajectory recognition experiments on three public datasets with a linear support vector machine classifier are conducted to verify the effectiveness and efficiency of the proposed approach.
Research Area(s)
- Kernel distance, motion trajectory recognition, multiscale self-similarities, three-dimensional/six-dimensional (3-D/6-D) motion trajectory descriptor
Citation Format(s)
On Multiscale Self-Similarities Description for Effective Three-Dimensional/Six-Dimensional Motion Trajectory Recognition. / Guo, Yao; Li, You-Fu; Shao, Zhanpeng.
In: IEEE Transactions on Industrial Informatics, Vol. 13, No. 6, 8031346, 12.2017, p. 3017-3026.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review