BILTS: A novel bi-invariant local trajectory-shape descriptor for rigid-body motion
arxiv(2024)
摘要
Measuring the similarity between motions and established motion models is
crucial for motion analysis, recognition, generation, and adaptation. To
enhance similarity measurement across diverse contexts, invariant motion
descriptors have been proposed. However, for rigid-body motion, few invariant
descriptors exist that are bi-invariant, meaning invariant to both the body and
world reference frames used to describe the motion. Moreover, their robustness
to singularities is limited. This paper introduces a novel Bi-Invariant Local
Trajectory-Shape descriptor (BILTS) and a corresponding dissimilarity measure.
Mathematical relationships between BILTS and existing descriptors are derived,
providing new insights into their properties. The paper also includes an
algorithm to reproduce the motion from the BILTS descriptor, demonstrating its
bidirectionality and usefulness for trajectory generation. Experimental
validation using datasets of daily-life activities shows the higher robustness
of the BILTS descriptor compared to the bi-invariant ISA descriptor. This
higher robustness supports the further application of bi-invariant descriptors
for motion recognition and generalization.
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