Real-time traffic sign recognition from video by class-specific discriminative features
In this paper we address the problem of traffic sign recognition. Novel image representation and discriminative feature selection algorithms are utilised in a traditional three-stage framework involving detection, tracking and recognition. The detector captures instances of equiangular polygons in t...
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| Published in: | Pattern recognition Vol. 43; no. 1; pp. 416 - 430 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
2010
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | In this paper we address the problem of traffic sign recognition. Novel image representation and discriminative feature selection algorithms are utilised in a traditional three-stage framework involving detection, tracking and recognition. The detector captures instances of equiangular polygons in the scene which is first appropriately filtered to extract the relevant colour information and establish the regions of interest. The tracker predicts the position and the scale of the detected sign candidate over time to reduce computation. The classifier compares a discrete-colour image of the observed sign with the model images with respect to the class-specific sets of discriminative local regions. They are learned off-line from the idealised template sign images, in accordance with the principle of one-vs-all dissimilarity maximisation. This dissimilarity is defined based on the so-called
Colour Distance Transform which enables robust discrete-colour image comparisons. It is shown that compared to the well-established feature selection techniques, such as Principal Component Analysis or AdaBoost, our approach offers a more adequate description of signs and involves effortless training. Upon this description we have managed to build an efficient road sign recognition system which, based on a conventional nearest neighbour classifier and a simple temporal integration scheme, demonstrates a competitive performance in the experiments involving real traffic video. |
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| AbstractList | In this paper we address the problem of traffic sign recognition. Novel image representation and discriminative feature selection algorithms are utilised in a traditional three-stage framework involving detection, tracking and recognition. The detector captures instances of equiangular polygons in the scene which is first appropriately filtered to extract the relevant colour information and establish the regions of interest. The tracker predicts the position and the scale of the detected sign candidate over time to reduce computation. The classifier compares a discrete-colour image of the observed sign with the model images with respect to the class-specific sets of discriminative local regions. They are learned off-line from the idealised template sign images, in accordance with the principle of one-vs-all dissimilarity maximisation. This dissimilarity is defined based on the so-called
Colour Distance Transform which enables robust discrete-colour image comparisons. It is shown that compared to the well-established feature selection techniques, such as Principal Component Analysis or AdaBoost, our approach offers a more adequate description of signs and involves effortless training. Upon this description we have managed to build an efficient road sign recognition system which, based on a conventional nearest neighbour classifier and a simple temporal integration scheme, demonstrates a competitive performance in the experiments involving real traffic video. |
| Author | Li, Yongmin Liu, Xiaohui Ruta, Andrzej |
| Author_xml | – sequence: 1 givenname: Andrzej surname: Ruta fullname: Ruta, Andrzej email: Andrzej.Ruta@brunel.ac.uk, mailme@aruta.pl – sequence: 2 givenname: Yongmin surname: Li fullname: Li, Yongmin email: Yongmin.Li@brunel.ac.uk – sequence: 3 givenname: Xiaohui surname: Liu fullname: Liu, Xiaohui email: Xiaohui.Liu@brunel.ac.uk |
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| Keywords | Forward feature selection Traffic sign recognition Computer vision-based driver assistance Colour Distance Transform Discriminative local regions Performance evaluation Automatic classification Similarity Image processing Learning Polygonal shape Image representation Driver information systems Learning algorithm Nearest neighbour Computer vision Discriminant analysis Target tracking Teletraffic Distance transformation Signal representation Signal classification Color image Statistical method Interest region Image analysis Driver assistance Signal processing Feature extraction Open market Principal component analysis |
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| SubjectTerms | Applied sciences Artificial intelligence Colour Distance Transform Computer science; control theory; systems Computer vision-based driver assistance Discriminative local regions Exact sciences and technology Forward feature selection Image processing Information, signal and communications theory Miscellaneous Pattern recognition. Digital image processing. Computational geometry Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Traffic sign recognition |
| Title | Real-time traffic sign recognition from video by class-specific discriminative features |
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