Through-Wall Human Pose Reconstruction via UWB MIMO Radar and 3D CNN

Song, Yongkun; Jin, Tian; Dai, Yongpeng; Song, Yongping; Zhou, Xiaolong

doi:10.3390/rs13020241

Cited by 40 publications

(16 citation statements)

References 48 publications

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“…Skeletal estimation utilizing radar devices represents a burgeoning area of research. Radar-based devices can be broadly categorized into two groups: high-frequency radars, such as millimeter-wave (mmWave) or terahertz radars [1,3,[11][12][13][14][15][16][17], and lower frequency radars, operating around a few GHz [18][19][20][21][22][23][24]. High-frequency radar signals, with their shorter wavelengths, provide greater precision in posture capture but lack the ability to penetrate walls and furniture.…”

Section: Introductionmentioning

confidence: 99%

“…Pioneering work by MIT researchers [18][19][20] introduced a neural network system that interprets radar signals for 2D human pose and dynamic 3D human mesh estimation. Jin et al [21] developed a novel through-wall 3D pose reconstruction framework using UWB MIMO radar and 3D CNNs for concealed target detection. Fang et al [22] proposed a cross-modal CNN-based method for postural reconstruction in Through the Wall Radar Imaging (TWRI).…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar

Zhou,

Jin,

Dai

et al. 2024

Remote Sensing

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In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar

Zhou,

Jin,

Dai

et al. 2024

Remote Sensing

Self Cite

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show abstract

“…In [15], the authors proposed the first model, RF-Pose3D, for generating 3D human skeletons from radar signals. Yongkun Song et al [16] were the first to propose a pose reconstruction method using low-frequency ultra-wideband MIMO radar as a detection sensor. Zhijie Zheng et al [17,18] proposed RPSNet based on cross-modal learning to achieve human skeleton and shape recovery.…”

Section: Introductionmentioning

confidence: 99%

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar

Lin,

Hu,

Xie

et al. 2023

Sensors

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Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range–time–Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

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“…Due to the interference suppression ability and excellent detection resolution, MIMO radar has gained a lot of attention in recent years. MIMO radar has been widely applied to Vehicle radar for driving-assistance [5][6][7], human posture detection [8,9], and perimeter security [10]. References [11,12] utilise the waveform diversity characteristic of MIMO radar to achieve multi-mode synthetic aperture radar (SAR) imaging.…”

Section: Introductionmentioning

confidence: 99%

A GPU‐based real‐time processing system for frequency division multiple‐input‐multiple‐output radar

Liu,

Bao,

Yue

et al. 2023

IET Radar Sonar & Navi

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Multiple‐Input‐Multiple‐Output (MIMO) radar has the characteristic of multiple antenna channels, bringing on a big data volume for signal processing. Therefore, the trade‐off between detection ability and computational efficiency is always considered. In this article, an optimisation system is proposed to enhance the real‐time performance of frequency division MIMO radar without compromising accuracy. For the scenario of low‐altitude small target detection, a signal processing acceleration method is proposed and a MIMO radar optimisation system based on graphics processing unit (GPU) architecture is built. The signal model of frequency division MIMO radar is first established, improving the classical signal processing flow efficiency from the perspective of reducing fast Fourier transform (FFT) times and windowing operation times. To achieve an advanced acceleration, the parallel architecture of ArrayFire‐library in GPU is then employed. Distinct minimum parallel units are extracted by analysing the principle of digital beamforming (DBF), pulse compression, moving target detection (MTD), and constant false‐alarm rate (CFAR) algorithms. And the corresponding parallel algorithms are designed to constitute a parallel acceleration system of frequency division MIMO. Simulation results indicate that the proposed method significantly improves the efficiency of MIMO system with a maximum acceleration ratio of 65 times, meeting the real‐time processing requirements.

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Through-Wall Human Pose Reconstruction via UWB MIMO Radar and 3D CNN

Cited by 40 publications

References 48 publications

Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar

Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar

A GPU‐based real‐time processing system for frequency division multiple‐input‐multiple‐output radar

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