  In most agent-based simulators, pedestrians navigate from origins to
destinations. Consequently, destinations are essential input parameters to the
simulation. While many other relevant parameters as positions, speeds and
densities can be obtained from sensors, like cameras, destinations cannot be
observed directly. Our research question is: Can we obtain this information
from video data using machine learning methods? We use density heatmaps, which
indicate the pedestrian density within a given camera cutout, as input to
predict the destination distributions. For our proof of concept, we train a
Random Forest predictor on an exemplary data set generated with the Vadere
microscopic simulator. The scenario is a crossroad where pedestrians can head
left, straight or right. In addition, we gain first insights on suitable
placement of the camera. The results motivate an in-depth analysis of the
methodology.
