Cited by 12Gait Recognition and AnalysisHuman Pose and Action RecognitionVideo Surveillance and Tracking Methods

Nonstandard Periodic Gait Energy Image for Gait Recognition and Data Augmentation

Wang, Kejun, Liu, Liangliang, Lee, Yilong, Ding, Xinnan, Lin, Junyu·2019·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-030-31723-2_17

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20 of 20 references verified0 citation errors · 0 not checkable

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  • Wang et al. employed nonstandard periodic GEI approaches for gait recognition and data augmentation.

    “A similar work proposed by Wang et al. [113] employ nonstandard periodic GEI approaches for gait recognition and data augmentation.”

    From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [113]

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Authors
Wang, K., Liu, L., Lee, Y., Ding, X., & Lin, J.
Affiliations
  • Harbin Engineering University (CN)
  • Chinese Academy of Sciences (CN)
Corresponding author
Wang, Kejun
Venue
Lecture Notes in Computer Science
Published
2019
Pages
197-208
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2987501385
Cited by
12
References
20
Citations by year
2025: 1 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 1 · 2020: 1
FWCI
2.60
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
gait energy image, gait recognition, gait cycle detection, data augmentation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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