Eigen-based traffic sign recognition

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Eigen-based traffic sign recognition

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This study's purpose is to introduce eigen-based traffic sign recognition. This technique is based on invoking the principal component analysis (PCA) algorithm to choose the most effective components of traffic sign images to classify an unknown traffic sign. A set of weights are computed from the most effective eigen vectors of the traffic sign. By using the Euclidean distance, unknown traffic sign images are then classified. The approach was tested on two different databases of traffic sign's borders and speed limit pictograms that were extracted automatically from real-world images. A classification rate of 96.8 and 97.9% was achieved for these two databases. To check the robustness of this approach, non-traffic sign objects and occluded signs were invoked. A performance of 71% was achieved when occluded signs are used. When signs were rotated 10 degrees around their centre, the performance became 89% when traffic signs' outer shapes were used and for rotated speed limit pictograms the result was 80%.

Inspec keywords: driver information systems; image classification; principal component analysis; geometry; object recognition

Other keywords: traffic sign borders; driver support systems; intelligent vehicles; Euclidean distance; speed limit pictograms; principal component analysis algorithm; traffic sign image classification; eigen-based traffic sign recognition

Subjects: Other topics in statistics; Combinatorial mathematics; Image recognition; Combinatorial mathematics; Computer vision and image processing techniques; Other topics in statistics; Traffic engineering computing

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