Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System
A region of interest (ROI) that may contain vehicles is extracted based on the composite features on vehicle’s bottom shadow and taillights by setting a gray threshold on vehicle shadow region and a series of constraints on taillights. In order to identify the existence of target vehicle in front of...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8842297 |
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| _version_ | 1832550090530619392 |
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| author | Xuewen Chen Huaqing Chen Huan Xu |
| author_facet | Xuewen Chen Huaqing Chen Huan Xu |
| author_sort | Xuewen Chen |
| collection | DOAJ |
| description | A region of interest (ROI) that may contain vehicles is extracted based on the composite features on vehicle’s bottom shadow and taillights by setting a gray threshold on vehicle shadow region and a series of constraints on taillights. In order to identify the existence of target vehicle in front of Advanced Driver Assistance System (ADAS) for the extracted ROI, a neural network recognizer of the Radial Basis Function (RBF) is found by extracting a series of parameters on the vehicle’s edge and region features. Using a large amount of collected images, the ROI that may contain vehicles is verified to be effective by extracting composite features of the shadow at the bottom of vehicle and taillights. Based on the positive and negative sample base of vehicles, the neural network recognizer is trained and learned, which can quickly realize network convergence. Furthermore, the vehicle can be effectively identified in the region of interest using the trained network. Test results show that the vehicle detection method based on multifeature extraction and recognition method based on RBF network have stable performance and high recognition accuracy. |
| format | Article |
| id | doaj-art-a463d554fac24209986634720b9372a8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a463d554fac24209986634720b9372a82025-02-03T06:07:41ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88422978842297Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS SystemXuewen Chen0Huaqing Chen1Huan Xu2College of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaCollege of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaCollege of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaA region of interest (ROI) that may contain vehicles is extracted based on the composite features on vehicle’s bottom shadow and taillights by setting a gray threshold on vehicle shadow region and a series of constraints on taillights. In order to identify the existence of target vehicle in front of Advanced Driver Assistance System (ADAS) for the extracted ROI, a neural network recognizer of the Radial Basis Function (RBF) is found by extracting a series of parameters on the vehicle’s edge and region features. Using a large amount of collected images, the ROI that may contain vehicles is verified to be effective by extracting composite features of the shadow at the bottom of vehicle and taillights. Based on the positive and negative sample base of vehicles, the neural network recognizer is trained and learned, which can quickly realize network convergence. Furthermore, the vehicle can be effectively identified in the region of interest using the trained network. Test results show that the vehicle detection method based on multifeature extraction and recognition method based on RBF network have stable performance and high recognition accuracy.http://dx.doi.org/10.1155/2020/8842297 |
| spellingShingle | Xuewen Chen Huaqing Chen Huan Xu Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System Complexity |
| title | Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System |
| title_full | Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System |
| title_fullStr | Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System |
| title_full_unstemmed | Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System |
| title_short | Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System |
| title_sort | vehicle detection based on multifeature extraction and recognition adopting rbf neural network on adas system |
| url | http://dx.doi.org/10.1155/2020/8842297 |
| work_keys_str_mv | AT xuewenchen vehicledetectionbasedonmultifeatureextractionandrecognitionadoptingrbfneuralnetworkonadassystem AT huaqingchen vehicledetectionbasedonmultifeatureextractionandrecognitionadoptingrbfneuralnetworkonadassystem AT huanxu vehicledetectionbasedonmultifeatureextractionandrecognitionadoptingrbfneuralnetworkonadassystem |