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Human Identification Based On Gait Manifold.pdf

WangXiuHui_Applied_Intelligence.pdf
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Summary

Researchers propose a gait-based pedestrian identification framework combining nonlinear dimensionality reduction via gait manifold and temporal features from gated recurrent units (GRUs). They design a temporal enhancement module to construct frame-by-frame gait trend energy images (ff-GTEIs) representing spatiotemporal gait characteristics. A supervised locally linear embedding (LLE) dimensionality reduction scheme generates a low-dimensional gait manifold for each pedestrian. A pedestrian identification network based on residual GRU identifies individuals by comparing gait and gait manifold similarities. Experiments on CASIA Dataset B and OU-ISIR LP dataset achieve correct recognition rates of 97.4% and 99.6%, respectively, exceeding most existing methods.

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Researchers propose a gait-based pedestrian identification framework combining nonlinear dimensionality reduction via gait manifold and temporal features from...

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  • File Format: PDF
  • File Size: 4.52 MB
  • Pages: 20
  • Language: EN
  • Total Downloads: 90
  • Last Updated: 11 hours ago

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