DeepActivity: a micro-Doppler spectrogram-based net for human behaviour recognition in bio-radar

DeepActivity: a micro-Doppler spectrogram-based net for human behaviour recognition in bio-radar

The movements of the human body and limbs result in unique micro-Doppler signatures, which can be exploited for classifying human activities. In this work, the authors propose a Convolutional Gated Recurrent Units Neural Network (CNN-GRU) to classify human activities of varying duration based on mic...

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Bibliographic Details
Main Authors: Hao Du, Tian Jin, Yongping Song, Yongpeng Dai
Format: Article
Language:English
Published: Wiley 2019-07-01
Series:The Journal of Engineering
Subjects:
feature extraction
image classification
Doppler radar
recurrent neural nets
radar imaging
convolutional neural nets
image coding
image motion analysis
human behaviour recognition
bio-radar
human body
limbs
microDoppler signatures
conventional deep learning approaches
temporally localising activity sequence
human activity classification
activity sequence recognition
DeepActivity
microDoppler spectrogram-based net
convolutional gated recurrent units neural Network
local feature extraction
convolutional layer
CNN-GRU model
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0145

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0145

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