A rapid learning algorithm for vehicle classification
•A fast learning algorithm is introduced for real-time vehicle classification.•A fast feature selection method for AdaBoost is presented by combining a sample’s feature value with its class label.•A rapid incremental learning algorithm of AdaBoost is designed. AdaBoost is a popular method for vehicl...
Uloženo v:
| Vydáno v: | Information sciences Ročník 295; s. 395 - 406 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
20.02.2015
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| Témata: | |
| ISSN: | 0020-0255 |
| On-line přístup: | Získat plný text |
| Tagy: |
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Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
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| Abstract | •A fast learning algorithm is introduced for real-time vehicle classification.•A fast feature selection method for AdaBoost is presented by combining a sample’s feature value with its class label.•A rapid incremental learning algorithm of AdaBoost is designed.
AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 32×32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle’s appearance. Then, a fast training approach for the weak classifier is presented by combining a sample’s feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications. |
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| AbstractList | AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 3232 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle's appearance. Then, a fast training approach for the weak classifier is presented by combining a sample's feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications. •A fast learning algorithm is introduced for real-time vehicle classification.•A fast feature selection method for AdaBoost is presented by combining a sample’s feature value with its class label.•A rapid incremental learning algorithm of AdaBoost is designed. AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 32×32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle’s appearance. Then, a fast training approach for the weak classifier is presented by combining a sample’s feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications. |
| Author | Shao, Ling Wen, Xuezhi Xue, Yu Fang, Wei |
| Author_xml | – sequence: 1 givenname: Xuezhi surname: Wen fullname: Wen, Xuezhi organization: Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 2 givenname: Ling surname: Shao fullname: Shao, Ling email: ling.shao@ieee.org organization: Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK – sequence: 3 givenname: Yu surname: Xue fullname: Xue, Yu organization: Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 4 givenname: Wei surname: Fang fullname: Fang, Wei organization: Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Snippet | •A fast learning algorithm is introduced for real-time vehicle classification.•A fast feature selection method for AdaBoost is presented by combining a... AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to... |
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| SubjectTerms | AdaBoost Algorithms Classification Haar-like features Incremental learning Learning Machine learning Real time State of the art Training Vehicle classification Vehicles Weak classifier |
| Title | A rapid learning algorithm for vehicle classification |
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