Optimal performance-efficiency trade-off for bag of words classification of road signs

This paper focuses on road sign classification for creating accurate and up-to-date inventories of traffic signs, which is important for road safety and maintenance. This is a challenging multi-class classification task, as a large number of different sign types exist which only differ in minor details. Moreover, changes in viewpoint, capturing conditions and partial occlusions result in large intra-class variations. Ideally, road sign classification systems should be robust against these variations, while having an acceptable computational load. This paper presents a classification approach based on the popular Bag Of Words (BOW) framework, which we optimize towards the best trade-off between performance and execution time. We analyze the performance aspects of PCA-based dimensionality reduction, soft and hard assignment for BOW codebook matching and the codebook size. Furthermore, we provide an efficient implementation scheme. We compare these techniques to design a fast and accurate BOW-based classification scheme. This approach allows for the selection of a fast but accurate classification methodology. This BOW approach is compared against structural classification, and we show that their combination outperforms both individual methods. This combination, exploiting both BOW and structural information, attains high classification scores (96.25% to 98%) on our challenging real-world datasets.