Real Time Detection and Recognition of Road Traffic Signs
Dr. Jack Greenhalgh and Prof. Majid Mirmehdi
We researched automatic detection and recognition of text in traffic signs. Scene structure is used to define search regions within the image, in which traffic sign candidates are then found. Maximally stable extremal regions (MSER) and hue, saturation, value (HSV) colour thresholding are used to locate a large number of candidates, which are then reduced by applying constraints based on temporal and structural information. A recognition stage interprets the text contained within detected candidate regions. Individual text characters are detected as MSERs and grouped into lines before being interpreted using optical character recogntion (OCR). Recognition accuracy is vastly improved through the temporal fusion of text results across consecutive frames.
Jack Greenhalgh, Majid Mirmehdi, Detection and Recognition of Painted Road Markings. 4th International Conference on Pattern Recognition Applications and Methods, January 2015, Lisbon, Portugal. [pdf]
Jack Greenhalgh, Majid Mirmehdi, Recognizing Text-Based Traffic Signs. Transactions on Intelligent Transportation Systems, 16 (3), 1360-1369, 2015 [pdf]
Jack Greenhalgh, Majid Mirmehdi, Automatic Detection and Recognition of Symbols and Text on the Road Surface, Pattern Recognition: Applications and Methods, 124-140, 2015
Jack Greenhalgh, Majid Mirmehdi, Real Time Detection and Recognition of Road Traffic Signs. Transactions on Intelligent Transportation Systems, Vol.13, no.4, pp.1498-1506, Dec.2012 [pdf]
Jack Greenhalgh, Majid Mirmehdi, Traffic Sign Recognition Using MSER and Random Forests. 20th European Signal Processing Conference, pages 1935-1939. EURASIP, August 2012, Bucharest, Romania. [pdf]
Here is some data for the detction and recognition of text-based road signs. This dataset consists of 9 video sequences, with a total of 23,130 frames, at a resolution of 1920 X 1088 pixels. Calibration parameters for the camera used to capture the data are also provided.