Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

Zhang, Guangcheng; Li, Shenchen; Zhang, Kai; Lin, Yueh‐Jaw

doi:10.3390/s23167208

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…In recent years, remarkable strides have been made in the areas of robotic sensing technologies and machine learning methods [1][2][3]. These advancements have notably bolstered the reliability of perception systems, with instances such as LiDAR point clouds [4][5][6][7] and the integration of multisensor data [8][9][10], thus showcasing exceptional performance within the domain of vehicle perception.…”

Section: Introductionmentioning

confidence: 99%

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm

Chen,

Wang,

Liu

et al. 2024

Sensors

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Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.

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Section: Introductionmentioning

confidence: 99%

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm

Chen,

Wang,

Liu

et al. 2024

Sensors

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Intelligent wheelchair human–robot interactive system based on human posture recognition

Han,

Zhou,

Jiang

et al. 2024

J Mech Sci Technol

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A target recognition algorithm using machine learning based on millimeter wave radar on intelligent connected vehicles

Fan,

Liu,

Wei

et al. 2024

Meas. Sci. Technol.

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Recognition technology based on millimeter wave radar (MMW) can operate in all-weather conditions and has received muchattention in the field of intelligent connected vehicles (ICV). However, the label information of the targets cannot be directly obtained from the original radar point clouds, making it necessary to develop advanced recognition algorithms. This paper proposes a target recognition algorithm based on machine learning (ML) that utilizes radar point clouds and leverages the radar reflection intensity to improve target recognition accuracy. Firstly, regional division and density clustering techniques are employed to preprocess the original point clouds from the MMW and segment them into meaningful regions, thereby reducing the computational burden; Secondly, relevant features are extracted from the processed radar point cloud, including RCS and its related features. Finally, to improve target recognition accuracy, this paper proposes a grid search optimization principal component analysis support vector machine (GS-PCA-SVM) classification algorithm. The algorithm uses PCA to reduce the dimensionality of the data while preserving key information; then, it optimizes the parameters and kernel function of SVM by using the grid search method to improve the performance of the classifier. The experimental results indicate that the recognition algorithm proposed in this paper achieves accuracies of 80%, 93%, and 95% on static, dynamic, and mixed datasets, respectively. Real vehicle experiments also prove that this algorithm has high accuracy and reliability when applied to ICV.

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Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

Cited by 4 publications

References 41 publications

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm

Intelligent wheelchair human–robot interactive system based on human posture recognition

A target recognition algorithm using machine learning based on millimeter wave radar on intelligent connected vehicles

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