Pose-based multisource networks using convolutional neural network and long short-term memory for action recognition
Author:
Affiliation:
1. Nanjing Tech University, School of Computer Science and Thechnology, Nanjing
2. China University of Mining and Technology, School of information and Control Engineering, Xuzhou
Publisher
SPIE-Intl Soc Optical Eng
Subject
Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics
Reference49 articles.
1. P-CNN: pose-based CNN features for action recognition;Cheron,2015
2. Pose for action—action for pose;Iqbal,2016
3. 3D Convolutional Neural Networks for Human Action Recognition
4. Imagenet classification with deep convolutional neural networks;Krizhevsky,2012
5. Skeleton-based action recognition with convolutional neural networks;Li,2017
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1. Human pose evaluation based on full-domain convolution and LSTM;Applied Mathematics and Nonlinear Sciences;2023-10-17
2. Feature difference and feature correlation learning mechanism for skeleton-based action recognition;Journal of Electronic Imaging;2023-01-30
3. ActionFormer: Pose-based Action Recognition via Transformer and CNN;2022 China Automation Congress (CAC);2022-11-25
4. Two-stream spatial-temporal neural networks for pose-based action recognition;Journal of Electronic Imaging;2020-08-26
5. Learning spatial–temporal features via a pose-flow relational model for action recognition;AIP Advances;2020-07-01
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