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Improving gait recognition with 3D pose estimation
Weizhi An
, Rijun Liao
, Shiqi Yu
*
, Yongzhen Huang
,
Pong Chi YUEN
*
Corresponding author for this work
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
64
Citations (Scopus)
Overview
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Dive into the research topics of 'Improving gait recognition with 3D pose estimation'. Together they form a unique fingerprint.
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Keyphrases
3D Pose Estimation
100%
3D Pose
100%
Gait Recognition
100%
Gait
66%
View Variation
66%
Model-based Method
66%
2D Image
33%
Human Body
33%
Recognition Rate
33%
Temporal Features
33%
Biometric Features
33%
Clothing
33%
Pose Estimation
33%
Body Joints
33%
Spatial Features
33%
Pose-based
33%
2D Pose
33%
Appearance-based
33%
Human Identification
33%
Engineering
Gait Recognition
100%
3D pose estimation
100%
Experimental Result
66%
Convolutional Neural Network
33%
Recognition Rate
33%
Pose Estimation
33%
Spatial Feature
33%
Body Joint
33%
Biometric Feature
33%
Lstm
33%
Computer Science
Pose Estimation
100%
Gait Recognition
100%
Experimental Result
66%
Convolutional Neural Network
33%
Recognition Rate
33%
Biometric Feature
33%
Long Short-Term Memory Network
33%
Chemical Engineering
Long Short-Term Memory Network
100%