We present a processing pipeline for semantic scene labelling that was developed in view of autonomous driving applications. Our study focuses on two different methods for feature selection - Texture-layout-filter (TLF) and Single Histogram Class Models (SHCM) - whose influence on the performance of a random forest classifier is investigated. In tests on the Cityscapes dataset, we assess the effects of parameter variation and observe an improvement of the Intersection over Union score by 44 percent when substituting the TLF by the computationally more demanding SHCM feature.
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