Abstract

As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios, such as children playing in the street or people using bicycles/skateboards in unexpected ways. Such “inthe-tail” data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset.