Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity

Alnujaim, Ibrahim; Oh, Daegun; Kim, Youngwook

doi:10.1109/igarss.2019.8898073

Cited by 22 publications

(12 citation statements)

References 7 publications

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“…Image recognition tasks for Convolutional Neural Network image classification are affected by data scarcity due to their data requirements [26,27], where many generative models have been recommended to alleviate such issues [28,29]. Generative models have also been noted to positively impact biological signal classification [30,31], semantic Imageto-Image Translation [32], speech processing [33,34], and Human Activity Recognition [35,36] among many others.…”

Section: Data Scarcity and Augmentationmentioning

confidence: 99%

Fruit quality and defect image classification with conditional GAN data augmentation

Bird

Barnes

Manso

et al. 2022

Scientia Horticulturae

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Section: Data Scarcity and Augmentationmentioning

confidence: 99%

Fruit quality and defect image classification with conditional GAN data augmentation

Bird

Barnes

Manso

et al. 2022

Scientia Horticulturae

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“…The work in this paper is developed concurrently with that of [33] [34] and provides, to our knowledge, the first in-depth analysis of GAN-generated synthetic data in terms of kinematic fidelity and diversity. In particular, we propose the utilization of auxillary classifier generative adversarial networks (ACGANs) [35], as opposed to conditional variational autoencoders (CVAEs) [36], for the generation of synthetic micro-Doppler signatures with greater diversity and sharpness.…”

Section: Introductionmentioning

confidence: 99%

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

Erol

Gürbüz

Amin

2020

IEEE Trans. Aerosp. Electron. Syst.

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Deep neural networks (DNNs) have recently received vast attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, and biomedicine. However, acquiring a sufficiently large training dataset remains a daunting task due to the high human costs and resources required for radar data collection. In this paper, an extended approach to adversarial learning is proposed for generation of synthetic radar micro-Doppler signatures that are well-adapted to different environments. The synthetic data is evaluated using visual interpretation, analysis of kinematic consistency, data diversity, dimensions of the latent space, and saliency maps. A principlecomponent analysis (PCA) based kinematic-sifting algorithm is introduced to ensure that synthetic signatures are consistent with physically possible human motions. The synthetic dataset is used to train a 19-layer deep convolutional neural network (DCNN) to classify micro-Doppler signatures acquired from an environment different from that of the dataset supplied to the adversarial network. An overall accuracy 93% is achieved on a dataset that contains multiple aspect angles (0 • , 30 • , and 45 • as well as 60 • ), with 9% improvement as a result of kinematic sifting.B. Erol is currently with Siemens Corporate Technology, Princeton, NJ. Formerly, he was with the Center for Advanced Communications, Villanova University, PA, email: baris.erol@siemens.com.S.Z. Gurbuz is with the

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“…An alternative approach for generating synthetic data, which can potentially bridge this deficiency, is adversarial learning. [27][28][29] Recent studies 27,30 show that auxiliary conditional GANs (ACGANs) are effective in modeling both sensor imperfections and clutter in a through-the-wall sensing scenario. However, the main challenge in application of GANs has been the lack of kinematic fidelity in a significant percentage of signatures generated.…”

Section: Introductionmentioning

confidence: 99%

Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

Gürbüz

Rahman

Kurtoğlu

et al. 2020

Radar Sensor Technology XXIV

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Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature to address this problem, such as unsupervised pre-training and transfer learning from optical imagery or synthetic RF data. This work investigates an alternative approach to training which involves exploitation of "datasets of opportunity"-micro-Doppler datasets collected using other RF sensors, which may be of a different frequency, bandwidth or waveform-for the purposes of training. Specifically, this work compares in detail the cross-frequency training degradation incurred for several different training approaches and deep neural network (DNN) architectures. Results show a 70% drop in classification accuracy when the RF sensors for pre-training, fine-tuning, and testing are different, and a 15% degradation when only the pre-training data is different, but the fine-tuning and test data are from the same sensor. By using generative adversarial networks (GANs), a large amount of synthetic data is generated for pre-training. Results show that cross-frequency performance degradation is reduced by 50% when kinematically-sifted GAN-synthesized signatures are used in pre-training.

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Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity

Cited by 22 publications

References 7 publications

Fruit quality and defect image classification with conditional GAN data augmentation

Fruit quality and defect image classification with conditional GAN data augmentation

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

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