Road-Sign Detection and Recognition Based on Support Vector Machines

By Saturnino Maldonado-Bascón, Sergio Lafuente-Arroyo, Pedro Gil-Jiménez, Hilario Gómez-Moreno, Francisco López-Ferreras. IEEE transactions on intelligent transportation systems (Print), 2007

743 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/a577a1bf39ab2fda6802ee0441b7429d17eb2353

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Citations in the corpus

Traffic sign detection and recognition using fully convolutional network guided proposals (2016, methodology): “In [9], candidate blobs are ﬁ rst detected by thresholding HSI color, then the detected blobs are classi ﬁ ed by linear SVMs according to shape.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “Furthermore, some other methods were proposed, such as distance to bounding box [85], fast Fourier transform (FFT) of shape signatures [86], tangent functions [71], simple image patches [70], and combinations of various simple features [77].”
Traffic Sign Recognition via Multi-Modal Tree-Structure Embedded Multi-Task Learning (2017, methodology): “In [21], a kernel rule is developed for road sign classification using the Laplace probability density, and in [22] Saturnino et al. perform the content recognition by employing Gaussian kernel SVMs. Kernel based methods learn the classifier by projecting the features into a high dimensional space,…”
Traffic-Sign Detection and Classification in the Wild (2016, methodology): “based on SVMs [18] and (a) 8192×2048 panorama from Tencent Street View before slicing vertically into 4 images.”
Knowledge-based Recurrent Attentive Neural Network for Traffic Sign Detection (2018, background): “, HSI/HSV transformation [23], region growing [24], color indexing [25], and YCbCr color space transform [26]).”
Object Detection in 20 Years: A Survey (2019, methodology): “detection methods are usually based on color thresholding [364–368], visual saliency detection[369], morphological filtering [79], and edge/contour analysis [370, 371].”
Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey (2012, methodology): “A somewhat similar system was put forth by [19], who uses a saliency measure to ﬁnd possible areas of interests.”
Recent progress in semantic image segmentation (2018, background): “Semantic image segmentation has multiple applications, such as detecting road signs (Maldonado-Bascon et al. 2007), colon crypts segmentation (Cohen et al. 2015), land use and land cover classification (Huang et al. 2002).”