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The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied. | The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied. | ||
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* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf] | * [https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-33212-8_18.pdf] | ||
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| + | * [http://www.springerlink.com/index/pdf/10.1007/978-3-642-33212-8_18 http://www.springerlink.com/index/pdf/10.1007/978-3-642-33212-8_18], | ||
| + | : [http://dx.doi.org/10.1007/978-3-642-33212-8_18 http://dx.doi.org/10.1007/978-3-642-33212-8_18] | ||
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| + | * [https://link.springer.com/chapter/10.1007%2F978-3-642-33212-8_18 https://link.springer.com/chapter/10.1007%2F978-3-642-33212-8_18], | ||
| + | : [https://www.scipedia.com/public/Hillebrand_et_al_2012a https://www.scipedia.com/public/Hillebrand_et_al_2012a], | ||
| + | : [https://dblp.uni-trier.de/db/conf/annpr/annpr2012.html#HillebrandKWK12 https://dblp.uni-trier.de/db/conf/annpr/annpr2012.html#HillebrandKWK12], | ||
| + | : [https://doi.org/10.1007/978-3-642-33212-8_18 https://doi.org/10.1007/978-3-642-33212-8_18], | ||
| + | : [https://rd.springer.com/chapter/10.1007/978-3-642-33212-8_18 https://rd.springer.com/chapter/10.1007/978-3-642-33212-8_18], | ||
| + | : [https://academic.microsoft.com/#/detail/200726266 https://academic.microsoft.com/#/detail/200726266] | ||
Latest revision as of 16:01, 21 January 2021
Abstract
The recognition of traffic signs in many state-of-the-art driver assistance systems is performed by statistical pattern classification methods. Traffic signs in European countries share many similarities but also vary in colour, size, and depicted symbols, making it hard to obtain one general classifier with good performance in all countries. Training separate classifiers for each country requires huge amounts of labelled training data. A well-trained classifier for one country can be adapted to other countries by semi-supervised learning methods to perform reasonably well with relatively low requirements regarding labelled training data. Self-training classifiers adapting themselves to unknown domains always risk that the adaption will become ineffective or even fail completely due to the occurrence of incorrectly labelled samples. To assure that self-training classifiers adapt themself correctly, advanced multi-classifier training methods like co-training are applied.
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Published on 01/01/2012
Volume 2012, 2012
DOI: 10.1007/978-3-642-33212-8_18
Licence: CC BY-NC-SA license
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