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The high variability of road sign appearance and the variety of different classes have made the recognition of pictograms a high computational load problem in traffic sign detection based on computer vision. In this paper false alarms are reduced significantly by designing a cascade filter based on boosting detectors and a generative classifier based on heterogeneity of texture. The false alarm filter allows us to discard many false positives using a reduced selection of features, which are chosen from a wide set of features. Filtering is defined as a binary problem, where all speed limit signs are grouped together against noisy examples and it is the previous stage to the input of a recognition module based on Support Vector Machines (SVMs). In a traffic sign recognition system, the number of candidate blobs detected is, in general, much higher than the number of traffic signs. As asymmetry is an inherent problem, we apply a different treatment for false negatives (FN) and false positives (FP). The global filter offers high accuracy. It achieves very low false alarm ratio with low computational complexity.

FALSE ALARM FILTERING IN A VISION TRAFFIC SIGN RECOGNITION SYSTEM - An Approach based on AdaBoost and Heterogeneity of Texture