Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM

Zhang, Yongqiang; Peng, Lixin; Ma, Guilei; Man, Menghua; Liu, Shanghe

doi:10.3389/fnbot.2022.903197

Cited by 10 publications

(2 citation statements)

References 19 publications

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“…Sensors are often used to sense human objects to avoid collisions and ensure safe driving behavior, such as cameras, LiDAR, and FMCW radar. The signal will be interfered with by other signal sources during the acquisition process [5,6]. The visual sensor will also be affected by the light [7].…”

Section: Introductionmentioning

confidence: 99%

A New Model for Human Running Micro-Doppler FMCW Radar Features

Zhang

et al. 2023

Applied Sciences

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Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model.

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Section: Introductionmentioning

confidence: 99%

A New Model for Human Running Micro-Doppler FMCW Radar Features

Zhang

et al. 2023

Applied Sciences

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“…Synthetic aperture radar (SAR) is a high-resolution microwave imaging and detection system (Xie et al, 2016(Xie et al, , 2017(Xie et al, , 2020Zhang et al, 2022), which has the advantage of all-weather, all-day, and harsh environment work; thus, it can observe the land and ocean in real time for a long time (López-Randulfe et al, 2021;Yu et al, 2022). In recent years, related technology research on ship identification using SAR images has received great attention in the field of marine remote sensing (Lang et al, 2022;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images

Xie

Jiang

et al. 2022

Front. Neurorobot.

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Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency.

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MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition

Xiang,

Lin,

et al. 2024

2024 14th International Conference on Pattern Recognition Systems (ICPRS)

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Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM

Cited by 10 publications

References 19 publications

A New Model for Human Running Micro-Doppler FMCW Radar Features

A New Model for Human Running Micro-Doppler FMCW Radar Features

High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images

MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition

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