GaitSlice: A gait recognition model based on spatio-temporal slice features

被引:0
|
作者
Li, Huakang [1 ,2 ]
Qiu, Yidan [3 ]
Zhao, Huimin [1 ,2 ]
Zhan, Jin [1 ,2 ]
Chen, Rongjun [1 ,2 ]
Wei, Tuanjie [1 ,2 ]
Huang, Zhihui [1 ,2 ]
机构
[1] School of Computer Science, Pattern Recognition and Intelligent System Laboratory, Guangdong Polytechnic Normal University, Guangzhou,510665, China
[2] Guangdong Key Laboratory of Intellectual Property and Big Data, Guangzhou,510665, China
[3] School of Psychology, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province,
基金
中国国家自然科学基金;
关键词
Pattern recognition - Semantics;
D O I
暂无
中图分类号
Q66 [生物力学]; Q811 [仿生学]; Q692 [];
学科分类号
1111 ;
摘要
Improving the performance of gait recognition under multiple camera views (i.e., cross-view gait recognition) and various conditions is urgent. From observation, we find that adjacent body parts are inter-related while walking, and each frame in a gait sequence possesses different degrees of semantic information. In this paper, we propose a novel model, GaitSlice, to analyze the human gait based on spatio-temporal slice features. Spatially, we design Slice Extraction Device (SED) to form top-down inter-related slice features. Temporally, we introduce Residual Frame Attention Mechanism (RFAM) to acquire and highlight the key frames. To better simulate reality, GaitSlice combines parallel RFAMs with inter-related slice features to focus on the features’ spatio-temporal information. We evaluate our model on CASIA-B and OU-MVLP gait datasets and compare it with six typical gait recognition models by using rank-1 accuracy. The results show that GaitSlice achieves high accuracy in gait recognition under cross-view and various walking conditions. © 2021 Elsevier Ltd
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