RPNet: Gait Recognition with Relationships Between Each Body-Parts
May 1, 2022·
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1 min read
Hao Qin
Zhenxue Chen
Qingqiang Guo
Q. M. Jonathan Wu
Mengxu Lu
Abstract
RPNet introduces a Part Feature Relationship Extractor for gait recognition, capturing multi-scale body-part features and adjacent part relationships to improve robustness across occlusion, clothing, and view variations.
Type
Publication
IEEE Transactions on Circuits and Systems for Video Technology
RPNet studies how relationships among body parts can improve gait recognition under challenging covariates.
Authors
Ph.D. Student in Artificial Intelligence
I am a Ph.D. student in the College of Computer Science and Technology at
Zhejiang University. My research focuses on 3D vision, 3D Gaussian Splatting,
3D-AIGC, and multi-agent systems, with broader interests in self-supervised
representation learning and embodied visual content creation.