This thesis introduces several novel methods for the detection and recognition of both text and symbols in road signs and road markings. Firstly, a method for the automatic detection and recognition of symbol-based road signs is presented. This algorithm detects candidate regions as maximally stable extremal regions (MSER), due to their robustness to lighting variations. Candidate regions are verified and classified during a recognition stage, which uses a cascade of Random Forests trained on histogram of oriented gradient (HOG) features. All training data used in this process is synthetically generated from template images available from an online database, eliminating the need for real footage data. The method retains a high accuracy, even at high vehicle speeds, and can operate under a range of weather conditions. The algorithm runs in real-time, at a processing rate of 20 frames per second, and recognises all road signs currently in use in the UK. Comparative results are provided to validate the performance. Secondly, a method is proposed for the automatic detection and recognition of text in road Signs. Search regions for road sign candidates are defined through exploitation of scene structure. A large number of candidate regions are located through a combination of MSER and hue, saturation, value (HSV) thresholding, which are then reduced through the analysis of temporal and structural features. The recognition stage of the algorithm then aims to interpret the text contained within the candidate regions. Text characters are first detected as MSERs, which are then grouped into lines and interpreted using optical character recognition (OCR). Temporal fusion is applied to the text results across consecutive frames, which vastly improves performance. Comparative analysis is provided to validate the performance of the method, and an overall F-measure of 0.87 is achieved. Finally, a method for the automatic detection and recognition of symbols and text painted on the road surface is presented. Candidates for symbols and text characters are detected in an inverse perspective mapping (IPM) transformed version of the frame, to remove the effect of perspective distortion. Detected candidate regions are then divided into symbols and words, so that they can be recognised using scparate classification stages. Temporal fusion is applied to both words and symbols in order to improve performance. The performance of the proposed method is validated using a challenging dataset of videos, and provides overall F-measures of 0.85 and 0.91 for text characters and symbols, respectively.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:685967 |
| Date | January 2015 |
| Creators | Greenhalgh, Jack |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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