Classification of micro-Doppler radar hand-gesture signatures by means of Chebyshev moments

Pallotta, Luca; Cauli, Michela; Clemente, Carmine; Fioranelli, Francesco; Giunta, Gaetano; Farina, A.

doi:10.1109/metroaerospace51421.2021.9511751

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…The study of image moments has aroused strong interest among researchers. Moments are widely applied in image reconstruction [1][2][3], image analysis, image indexing [4][5][6][7], digital image research [8][9][10], spectral image super-resolution mapping [11], hyperspectral target detection [12], radar target recognition [13,14], SAR target recognition [15], sound classification [16], and other fields. Li et al [17] employed an innovative face recognition method that integrated the Gabor wavelet representation of face images with an enhanced discriminator, the Complete Kernel Fisher Discriminant (CKFD), and fractional power polynomial (FPP) models to improve recognition performance and discrimination ability.…”

Section: Introductionmentioning

confidence: 99%

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Gao,

Zhang,

Zhao

et al. 2024

Sensors

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In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score.

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Section: Introductionmentioning

confidence: 99%

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Gao,

Zhang,

Zhao

et al. 2024

Sensors

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“…This is one of the reasons why coherent radar systems have been preferred over Wi-Fi devices to capture the propagation phenomena caused by complex human activities. In the context of RF sensing, the recognition of human activity often relies on exploiting the micro-Doppler phenomenon [31]- [34] to discern the specific type of activity being performed. Thanks to recent advancements in the areas of radar techniques and machine/deep learning, the classification and tracking of a wide range of human activities in complex environments will be within reach in a few years.…”

Section: Introductionmentioning

confidence: 99%

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Waqar,

Muaaz,

Pätzold

2023

IEEE Sensors J.

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Modern monostatic radar-based human activity recognition (HAR) systems perform very well as long as the direction of human activities is either towards or away from the radar. The monostatic single-input single-output (SISO) and monostatic multipleinput multiple-output (MIMO) radar systems cannot detect motion of an object that moves perpendicularly to the radar's boresight axis. Due to this physical layer limitation, today's radar-based HAR systems fail to classify multi-directional human activities. In this paper, we resolve this typical but critical physical layer problem of contemporary HAR systems. We propose a HAR system underlying a distributed MIMO radar configuration, where multiple antennas of a millimeter wave MIMO radar system (Ancortek SDR-KIT 2400T2R4) are distributed in an indoor environment. In our proposed HAR system, we have two independent and identical monostatic radar subsystems that irradiate and capture the multi-directional human movement from two perspectives, which allows to compute two distinct time-variant radial velocity distributions. A feature extraction network extracts numerous features from the measured time-variant radial velocity distributions, which are then fused by a multiclass classifier to detect five types of human activities. The proposed multi-perspective MIMO-radar-based HAR system achieves a classification accuracy of 98.52%, which surpasses the accuracy of SISO radar-based HAR system by more than 9%. Our approach resolves the physical layer limitations of modern HAR systems that are based on either monostatic SISO or monostatic MIMO radar systems.

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“…The orthogonality properties of the Chebychev moments, together with the fact that they are defined in a discrete set, allow us to summarize in a feature vector the relevant information embedded in the CFD. As a matter of fact, they have already been widely used for classification purposes, like image or radar hand-gesture classification [18], [19]. The extracted image moments are then organized in a matrix form and used as input to the second branch of the designed FCN.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Environmental Sound Classification using Cadence Frequency Diagram and Chebychev Moments

Neri,

Pallotta,

Carli

2023

2023 International Symposium on Image and Signal Processing and Analysis (ISPA)

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The research conducted within the audio signal processing field is increasingly focusing on environmental sound classification. This paper presents a low-complexity Fully Convolutional Network composed of two parallel branches. These branches are responsible for extracting features from the Cadence Frequency Diagram representation and the Chebychev moments, respectively. By utilizing both domains of machineand deep-learning, the proposed pipeline takes advantage of the unique characteristics of each. The key strength of this architecture lies in its reduced number of layers and parameters, as well as its ability to efficiently compute the Cadence Frequency Diagram and Chebychev moments. The effectiveness of the proposed pipeline is demonstrated through various tests conducted on two audio datasets, namely UrbanSound8K and ESC-50.

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Classification of micro-Doppler radar hand-gesture signatures by means of Chebyshev moments

Cited by 11 publications

References 20 publications

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Low-Complexity Environmental Sound Classification using Cadence Frequency Diagram and Chebychev Moments

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