Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder

Le, Hoang Thanh; Phung, Son Lam; Bouzerdoum, A.

doi:10.1109/icpr.2018.8546044

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…More recently, some studies propose the combination of micro-Doppler data and deep learning algorithms for the gait-based human recognition [ 13 , 14 , 15 , 16 ]. However, the hierarchical structure of deep learning is more suitable to identify complicated patterns from raw data (i.e., images and signals) without any feature extraction [ 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…However, the hierarchical structure of deep learning is more suitable to identify complicated patterns from raw data (i.e., images and signals) without any feature extraction [ 24 , 25 ]. According to this, in [ 13 ], a deep autoencoder is used to perform human gait recognition with micro-Doppler radar. In this study, the best classification rate (96.2%) is obtained when a bayesian optimization is performed to identify the suitable hyperparameters combination.…”

Section: Related Workmentioning

confidence: 99%

“…The micro-Doppler classification capabilities are also confirmed in several other studies [ 7 , 8 , 9 , 10 , 11 , 12 ]. Since data produced by a FMCW data is particularly suitable for neural-networks processing, it is worthwhile investigating the adoption of Deep Learning (DL) algorithms for the gait-based human recognition using micro-Doppler signatures as features [ 13 , 14 , 15 , 16 ]. DL-based approaches extend classical machine learning ones using deeper neural networks that are capable of learning directly from more complex data, leading to better end-to-end classification and prediction performances.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition

Addabbo

Bernardi

Biondi

et al. 2021

Sensors

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The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition

Addabbo

Bernardi

Biondi

et al. 2021

Sensors

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“…Doppler radar, as shown in Figure 5a, is one of the most popular radars in HAR [20,[41][42][43]45,52,69]. A Doppler radar sends out single-tone radio waves and no modulation is involved.…”

Section: A Doppler Radarmentioning

confidence: 99%

“…As one of the sensor-based methods, radar-based HAR [20][21][22][23] has drawn much attention due to the following reasons. Firstly, radar is robust to light and weather conditions so it is able to be applied in harsh environments.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Deep Learning-Based Human Activity Recognition in Radar

Jing

2019

Remote Sensing

236

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Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.

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