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On the Construction of an Automatic Traffic Sign Recognition System
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis proposes an automatic road sign recognition system, including all steps from the initial detection of road signs from a digital image to the final recognition step that determines the class of the sign.

We develop a Bayesian approach for image segmentation in the detection step using colour information in the HSV (Hue, Saturation and Value) colour space. The image segmentation uses a probability model which is constructed based on manually extracted data on colours of road signs collected from real images. We show how the colour data is fitted using mixture multivariate normal distributions, where for the case of parameter estimation Gibbs sampling is used. The fitted models are then used to find the (posterior) probability of a pixel colour to belong to a road sign using the Bayesian approach. Following the image segmentation, regions of interest (ROIs) are detected by using the Maximally Stable Extremal Region (MSER) algorithm, followed by classification of the ROIs using a cascade of classifiers.

Synthetic images are used in training of the classifiers, by applying various random distortions to a set of template images constituting most road signs in Sweden, and we demonstrate that the construction of such synthetic images provides satisfactory recognition rates. We focus on a large set of the signs on the Swedish road network, including almost 200 road signs. We use classification models such as the Support Vector Machine (SVM), and Random Forest (RF), where for features we use Histogram of Oriented Gradients (HOG).

Place, publisher, year, edition, pages
2017. , p. 134
Keyword [en]
traffic sign recognition, object detection, image classification, mixture models, Gibbs sampling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-143593ISRN: LIU-IDA/STAT-A--17/002—SEOAI: oai:DiVA.org:liu-143593DiVA, id: diva2:1164420
External cooperation
VTI
Subject / course
Statistics
Supervisors
Examiners
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2017-12-11Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf