2009 · 12 citations · 0 references
ComputingComputer ScienceComputer VisionMachine LearningMachine VisionFeature DetectionObject DetectionObject RecognitionPattern RecognitionTraffic SafetyTraffic Sign RecognitionColor-based Traffic Sign RecognitionFeature Extraction For Traffic SignsShape-based Traffic Sign RecognitionSpatial SymmetrySubset SelectionTraffic Sign DetectionTraffic Sign Detection In ImagesWarning Of Danger
In this paper, we introduce a pre-detection algorithm dedicated to French danger-warning and prohibitory road signs. The proposed method combines color, shape, location and symmetry features to select among large image databases, a small subset of pictures that probably contain road signs. We report the results of a systematic experimental assessment that we performed on five image databases, comprised of more than 26,000 images, covering 176 km and containing 371 traffic signs, among which a non-negligible amount (about 5% in average) is damaged. The experiments show that about 10% images of the sequences are selected and more than 87% traffic signs are detected. The missed objects always correspond to dirty, worn-out or badly oriented signs that would be difficult to detect even for a human operator.