Catalogue-Based Traffic Sign Asset Management: Towards User’s Effort Minimisation
Automatic traffic sign recognition is a difficult task, as it is necessary to distinguish between a very high number of classes with low inter-class variability. The state-of-the-art methods report very high accuracy rates but just a few classes are covered and several training samples are required. For the sake of the development of an asset management system, these approaches are out of reach. Furthermore, in this context, minimizing user’s effort is more important than achieving maximal classification accuracy. In this paper, we propose a catalogue-based traffic sign classifier which doesn’t require real training samples for model building and promotes minimal user’s workload involving the catalogue’s semantic structure in the error propagation. Experimental results reveal that user’s workload was reduced by 20 % while accuracy was improved by 2 %.
KeywordsTraffic sign recognition Discriminative local regions Distance transform Traffic sign asset management User centered machine learning
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