Automatic Generation of Training Data for Image Classification of Road Scenes

There is an ever increasing demand for semantically annotated images to train and evaluate image classifiers. Currently this data is obtained by manually labeling each individual image thus making the process labor intensive and costly. We therefore present an approach that generates semantically annotated images for classes road and curb in a fully automatic way. The advantage of our method is that it relies on cues from range sensors only thus making its output suitable for training and evaluating image classifiers. We use a normal based curb detector and extract the road by running an active contour on these detections. Since our algorithm does not rely on parametric models it is possible to detect a wide range of road geometries in different environments. Sequences of up to a minute can be accurately labeled without any user interference in less than a minute.