Back to Search
Start Over
Through-Wall Human Motion Recognition Based on Transfer Learning and Ensemble Learning
- Source :
- IEEE Geoscience and Remote Sensing Letters. 19:1-5
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Human motion recognition based on ultra-wideband through-the-wall radar (UWB TWR) (a radar whose fractional bandwidth of the radar transmitted signal is bigger than 0.25) is faced with the problems of too few samples and the limitation of perspective. In this letter, we propose a multiradar cooperative human motion recognition model based on transfer learning and ensemble learning. Specifically, a ResNeXt network model based on transfer learning is first proposed to deal with the problem of too few samples. The model is pretrained on the public ImageNet database, and then it is transferred to the task of human motion recognition based on multiradar. Compared with a typical convolutional neural network from scratch, the ResNeXt network model based on transfer learning requires shorter epochs and achieves higher accuracy. Then, to solve the problem of model accuracy decline caused by the limitation of perspective, a multiradar human motion recognition model based on ensemble learning is proposed. Experimental results show that compared with the fusion model based on single-view radar, the recognition accuracy of network based on ensemble learning can be higher.
- Subjects :
- Computer science
business.industry
Perspective (graphical)
SIGNAL (programming language)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
Geotechnical Engineering and Engineering Geology
Convolutional neural network
Ensemble learning
law.invention
Task (project management)
law
Artificial intelligence
Electrical and Electronic Engineering
Radar
business
Transfer of learning
Network model
Subjects
Details
- ISSN :
- 15580571 and 1545598X
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Geoscience and Remote Sensing Letters
- Accession number :
- edsair.doi...........43f5170c9041543808dcd57dbd882347
- Full Text :
- https://doi.org/10.1109/lgrs.2021.3070374