Deep learning of micro-Doppler features for aided and unaided gait recognition

Seyfioğlu, Mehmet Saygın; Gürbüz, Sevgi Zübeyde; Özbayoğlu, A. Murat; Yüksel, Melda

doi:10.1109/radar.2017.7944373

Cited by 49 publications

(26 citation statements)

References 12 publications

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“…In [12], a similar method was used for hand gesture classification. More sophisticated DCNN architectures were used later, including 7-layer DCNN [13], transfer learned AlexNet and VGG-16 network [14] and a three-layer semisupervised auto-encoder [15]. New problems such as low latency classification [16] and multi-target human gait classification [9,17] have also been taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Chen

Fioranelli

et al. 2018

IEEE Geosci. Remote Sensing Lett.

141

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Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, (doi:10.1109/LGRS.2018.2806940) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/156827/ Abstract-In this letter we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in our work to improve classification accuracy. We first propose the voted monostatic DCNN method (VMo-DCNN), which trains DCNNs on each receiver node separately, and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN method (Mul-DCNN), which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4 GHz multistatic radar system. Experimental results show that Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node.

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Section: Introductionmentioning

confidence: 99%

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Chen

Fioranelli

et al. 2018

IEEE Geosci. Remote Sensing Lett.

141

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“…Principal Component Analysis (PCA) has been often exploited in radar applications, as an instrument to reduce the dimensionality of the available feature space and to automatize the feature extraction and selection procedure [24,25], together with deep learning algorithms for fall detection [26] and human activity recognition [27]. Recent works considered the application of deep learning techniques for gait classification, using smart sensors [28] and radar-based techniques [29] to discriminate aided from unaided motion.…”

Section: Related Workmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

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“…Three different neural network architectures have been exploreda downscaled version of the network VGG16, utilizing the same block structure; the very deep ResNET-50 [31], which uses shortcuts between network blocks to avoid overfitting and achieve better generalization; and an innovative CNN+LSTM (Long Short-Term Memory) architecture, which is able to extract features from micro-Doppler spectrogram segments, and learn their representation as time series (sequences of data). This is an innovative approach, as the radar data will be considered by the LSTM network part not as snapshot spectrograms images (as currently done in many works in the literature [22][23][24][25][26][27][28]), but as temporal data sequences. Although demonstrated on preliminary results on a small experimental dataset, this classification approach may prove well suited to radar data, exploiting the inherent information from a sequence of radar waveforms, rather than casting the problem as classification of images.…”

Section: Introductionmentioning

confidence: 99%

Practical classification of different moving targets using automotive radar and deep neural networks

Angelov

Robertson

Murray-Smith

et al. 2018

IET Radar, Sonar & Navigation

109

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In this work, we present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the 4 classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (approximately 0.55s to produce a 0.5s long spectrogram) are demonstrated in this paper, and possible shortcomings and outstanding issues are discussed.

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Deep learning of micro-Doppler features for aided and unaided gait recognition

Cited by 49 publications

References 12 publications

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

People Walking Classification Using Automotive Radar

Practical classification of different moving targets using automotive radar and deep neural networks

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