Unsupervised image transformation for outdoor semantic labelling

Semantic labelling of urban images is a crucial component towards autonomous driving. The accuracy of current methods is highly dependent on the training set being used and drops drastically when the distribution in the test image does not match the expected distribution of the training set. This situation will inevitably occur, as for instance, when the illumination changes from daytime to dusk. To address this problem we propose a fast unsupervised image transformation approach following a global color transfer strategy. Our proposal generalizes classical one-to-one color transfer schemes to the more suitable one-to-many scheme. In addition, our approach can naturally deal with the temporal consistency of video streams to perform a coherent transformation. We demonstrate the benefits of our proposal in two publicly available datasets using different state-of-the-art semantic labelling frameworks.