Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral
Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of veh...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/701058 |
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| _version_ | 1832547545328386048 |
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| author | Wenhui Li Peixun Liu Ying Wang Hongyin Ni |
| author_facet | Wenhui Li Peixun Liu Ying Wang Hongyin Ni |
| author_sort | Wenhui Li |
| collection | DOAJ |
| description | Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS). |
| format | Article |
| id | doaj-art-029c9db2cc5a4b22b69729c997d66b3a |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-029c9db2cc5a4b22b69729c997d66b3a2025-02-03T06:44:16ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/701058701058Multifeature Fusion Vehicle Detection Algorithm Based on Choquet IntegralWenhui Li0Peixun Liu1Ying Wang2Hongyin Ni3College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaVision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).http://dx.doi.org/10.1155/2014/701058 |
| spellingShingle | Wenhui Li Peixun Liu Ying Wang Hongyin Ni Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral Journal of Applied Mathematics |
| title | Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral |
| title_full | Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral |
| title_fullStr | Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral |
| title_full_unstemmed | Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral |
| title_short | Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral |
| title_sort | multifeature fusion vehicle detection algorithm based on choquet integral |
| url | http://dx.doi.org/10.1155/2014/701058 |
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