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AR3D: Attention Residual 3D Network for Human Action Recognition
- Source :
- Sensors, Vol 21, Iss 1656, p 1656 (2021), Sensors, Volume 21, Issue 5, Sensors (Basel, Switzerland)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN’s temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer. To solve this problem, this article is based on 3D CNN combined with a residual structure and attention mechanism to improve the existing 3D CNN model, and we propose two types of human action recognition models (the Residual 3D Network (R3D) and Attention Residual 3D Network (AR3D)). Firstly, in this article, we propose a shallow feature extraction module and improve the ordinary 3D residual structure, which reduces the parameters and strengthens the extraction of temporal features. Secondly, we explore the application of the attention mechanism in human action recognition and design a 3D spatio-temporal attention mechanism module to strengthen the extraction of global features of human action. Finally, in order to make full use of the residual structure and attention mechanism, an Attention Residual 3D Network (AR3D) is proposed, and its two fusion strategies and corresponding model structure (AR3D_V1, AR3D_V2) are introduced in detail. Experiments show that the fused structure shows different degrees of performance improvement compared to a single structure.
- Subjects :
- Computer science
Feature extraction
Video Recording
convolutional neural network
02 engineering and technology
010501 environmental sciences
Residual
lcsh:Chemical technology
01 natural sciences
Biochemistry
Convolutional neural network
Article
Field (computer science)
residual
Pattern Recognition, Automated
Analytical Chemistry
0202 electrical engineering, electronic engineering, information engineering
Humans
Human Activities
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
0105 earth and related environmental sciences
Structure (mathematical logic)
action recognition
business.industry
Pattern recognition
Atomic and Molecular Physics, and Optics
Action (philosophy)
Action recognition
020201 artificial intelligence & image processing
Neural Networks, Computer
Artificial intelligence
business
attention mechanism
3D
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 1656
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....8e4b792e37be62f460f59643e21da9c3