Traffic sign recognition using an extended bag-of-features model with spatial histogram

Traffic sign recognition (TSR) is a major challenging task for intelligent transport systems. In this paper, we present a multiclass traffic sign recognition system based on the Bag-of-Word (BOW) model. Despite huge success of BOW method, ignoring the spatial information is a weakness of this model and affects accuracy of classification. We have proposed a Spatial Histogram for traffic signs that preserves the required spatial information. In addition, we used an extended codebook construction method to extract key features from all of sign categories efficiently and achieved a recognition rate of %88.02 through 62 sign types with a short execution time.