Toward Complete-View and High-Level Pose-Based Gait Recognition
Author:
Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
2. Tencent AI Lab, Shenzhen, China
3. College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
Funder
National Natural Science Foundation of China
Shenzhen Research Council
Guangdong Natural Science Foundation
Shenzhen College Stability Support Plan
Shenzhen Science and Technology Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Safety, Risk, Reliability and Quality
Link
http://xplorestaging.ieee.org/ielx7/10206/9970396/10064100.pdf?arnumber=10064100
Reference69 articles.
1. Gait Recognition based on Local Graphical Skeleton Descriptor with Pairwise Similarity Network
2. A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video
3. View-Independent Gait Recognition Using Joint Replacement Coordinates (JRCs) and Convolutional Neural Network
4. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980,2014
5. Multi-Task GANs for View-Specific Feature Learning in Gait Recognition
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