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Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper,...

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Detalles Bibliográficos
Autores principales: Wu, Qisong, Gao, Teng, Lai, Zhichao, Li, Dianze
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349674/
https://www.ncbi.nlm.nih.gov/pubmed/32575841
http://dx.doi.org/10.3390/s20123504
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author Wu, Qisong
Gao, Teng
Lai, Zhichao
Li, Dianze
author_facet Wu, Qisong
Gao, Teng
Lai, Zhichao
Li, Dianze
author_sort Wu, Qisong
collection PubMed
description Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the [Formula: see text] score of [Formula: see text] and area under the curve (AUC) of the receiver operating characteristic (ROC) of [Formula: see text] over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar.
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spelling pubmed-73496742020-07-15 Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar † Wu, Qisong Gao, Teng Lai, Zhichao Li, Dianze Sensors (Basel) Article Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the [Formula: see text] score of [Formula: see text] and area under the curve (AUC) of the receiver operating characteristic (ROC) of [Formula: see text] over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar. MDPI 2020-06-21 /pmc/articles/PMC7349674/ /pubmed/32575841 http://dx.doi.org/10.3390/s20123504 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Qisong
Gao, Teng
Lai, Zhichao
Li, Dianze
Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †
title Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †
title_full Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †
title_fullStr Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †
title_full_unstemmed Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †
title_short Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †
title_sort hybrid svm-cnn classification technique for human–vehicle targets in an automotive lfmcw radar †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349674/
https://www.ncbi.nlm.nih.gov/pubmed/32575841
http://dx.doi.org/10.3390/s20123504
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