Neigel,P., Wasenmüller,O.
Abstract:
The detection of pedestrians plays an essential part in the development of automated driver assistance systems. Many of the currently available datasets for pedestrian detection focus on urban environments. State-of-the-art neural networks trained on these datasets struggle in generalizing their predictions from one environment to a visually dissimilar one, limiting the use case to urban scenes. Commercial working machines like tractors or excavators make up a substantial share of the total number of motorized vehicles and are often situated in fundamentally different surroundings, e.g. forests, meadows, construction sites or farmland. In this paper, we present a dataset for pedestrian detection which consists of 1018 stereo-images showing varying numbers of persons in differing non-urban environments and comes with manually annotated pixel-level segmentation masks and bounding boxes.