  Understanding and anticipating human activity is an important capability for
intelligent systems in mobile robotics, autonomous driving, and video
surveillance. While learning from demonstrations with on-site collected
trajectory data is a powerful approach to discover recurrent motion patterns,
generalization to new environments, where sufficient motion data are not
readily available, remains a challenge. In many cases, however, semantic
information about the environment is a highly informative cue for the
prediction of pedestrian motion or the estimation of collision risks. In this
work, we infer occupancy priors of human motion using only semantic environment
information as input. To this end we apply and discuss a traditional Inverse
Optimal Control approach, and propose a novel one based on Convolutional Neural
Networks (CNN) to predict future occupancy maps. Our CNN method produces
flexible context-aware occupancy estimations for semantically uniform map
regions and generalizes well already with small amounts of training data.
Evaluated on synthetic and real-world data, it shows superior results compared
to several baselines, marking a qualitative step-up in semantic environment
assessment.
