Fast shape-based road sign detection for a driver assistance system

By Gareth Blake Loy, Nick Barnes. IEEE/RJS International Conference on Intelligent RObots and Systems, 2004

371 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Traffic sign detection and recognition using fully convolutional network guided proposals (2016, neutral): “A large number of novel ideas and effective methods have been proposed [7 – 10, 12, 13, 16, 17, 21, 26 – 40 ].”
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.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “In [79], a fast radial symmetry transform was proposed to detect triangular, square or octagonal road signs.”
Road-Sign Detection and Recognition Based on Support Vector Machines (2007, background): “Nevertheless, some recent works (see [11] and [12]) have implemented a fast algorithm based on radial symmetry that is adapted to triangular, square, diamond, octagonal, and circular shapes.”
Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection (2018, methodology): “For the shape-based methods, Hough Transform [22], [23] and its variant [24]–[26] are commonly used to localize the various signs.”