Back to Search
Start Over
Human Motion Serialization Recognition With Through-the-Wall Radar
- Source :
- IEEE Access, Vol 8, Pp 186879-186889 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this article addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition.
- Subjects :
- General Computer Science
Computer science
Serialization
Feature extraction
0211 other engineering and technologies
auto-encoder network
Ultra-wideband
02 engineering and technology
Motion (physics)
law.invention
Dimension (vector space)
law
gated recurrent unit
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Computer vision
Radar
021101 geological & geomatics engineering
ultra wide band through-the-wall radar
business.industry
General Engineering
020206 networking & telecommunications
Human motion recognition
Support vector machine
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....6d2e992887130f947b12c85d169ea6d4