RPNet: Gait Recognition with Relationships Between Each Body-Parts

May 1, 2022·
秦皓
秦皓
Equal contribution
,
Zhenxue Chen
Equal contribution
,
Qingqiang Guo
,
Q. M. Jonathan Wu
,
Mengxu Lu
· 1 min read
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
publications

RPNet studies how relationships among body parts can improve gait recognition under challenging covariates.

Gait Recognition Computer Vision Deep Learning
秦皓
Authors
秦皓
Ph.D. Student at Zhejiang University

I am a Ph.D. student in the College of Computer Science and Technology at Zhejiang University. My research focuses on spatial intelligence, 3D-AIGC, multi-agent systems, latent reasoning for VLMs, and contrastive learning, with a broader interest in building intelligent systems that connect perception, reasoning, and controllable creation in the world.

Email: haoqin@zju.edu.cn