Salient traffic sign recognition based on sparse representation of visual perception
This paper proposes a new approach to recognize salient traffic signs, which is based on sparse representation of visual perception via visual saliency and speeded up robust features (SURF) algorithm. The proposed algorithm deals with two tasks: traffic signs detection and traffic signs recognition....
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Published in | 2012 International Conference on Computer Vision in Remote Sensing pp. 273 - 278 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.12.2012
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Abstract | This paper proposes a new approach to recognize salient traffic signs, which is based on sparse representation of visual perception via visual saliency and speeded up robust features (SURF) algorithm. The proposed algorithm deals with two tasks: traffic signs detection and traffic signs recognition. Firstly, multi-scale phase spectrum of quaternion Fourier transformation method is used to obtain the location of traffic signs in scenes image. Secondly, traffic signs local sparse features are extracted by the improved algorithm based on SURF descriptors and locality-constrained linear coding (LLC) method. Finally, linear support vector machine (SVM) is used to train classifier and test recognition accuracy rate of ban traffic signs. Extensive experiments on 1000 images show that our approach can improve recognition accuracy rate and reduce running time. |
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AbstractList | This paper proposes a new approach to recognize salient traffic signs, which is based on sparse representation of visual perception via visual saliency and speeded up robust features (SURF) algorithm. The proposed algorithm deals with two tasks: traffic signs detection and traffic signs recognition. Firstly, multi-scale phase spectrum of quaternion Fourier transformation method is used to obtain the location of traffic signs in scenes image. Secondly, traffic signs local sparse features are extracted by the improved algorithm based on SURF descriptors and locality-constrained linear coding (LLC) method. Finally, linear support vector machine (SVM) is used to train classifier and test recognition accuracy rate of ban traffic signs. Extensive experiments on 1000 images show that our approach can improve recognition accuracy rate and reduce running time. |
Author | Yaling Hu Lihua Tian Ce Li Limei Xiao |
Author_xml | – sequence: 1 surname: Ce Li fullname: Ce Li organization: Coll. of Electr. & Infonnation Eng., Lanzhou Univ. of Technol., Lanzhou, China – sequence: 2 surname: Yaling Hu fullname: Yaling Hu organization: Coll. of Electr. & Infonnation Eng., Lanzhou Univ. of Technol., Lanzhou, China – sequence: 3 surname: Limei Xiao fullname: Limei Xiao organization: Coll. of Electr. & Infonnation Eng., Lanzhou Univ. of Technol., Lanzhou, China – sequence: 4 surname: Lihua Tian fullname: Lihua Tian organization: Inst. of Artificial Intell. & Robot., Xi'an Jiao tong Univ., Xi'an, China |
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Snippet | This paper proposes a new approach to recognize salient traffic signs, which is based on sparse representation of visual perception via visual saliency and... |
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StartPage | 273 |
SubjectTerms | Accuracy Artificial neural networks Quaternion Fourier transform Robustness Sparse coding Support Vector Machine (SVM) Support vector machines Traffic sign detection Traffic sign recogntion Visual saliency |
Title | Salient traffic sign recognition based on sparse representation of visual perception |
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