  Recurrent neural networks are able to learn complex long-term relationships
from sequential data and output a pdf over the state space. Therefore,
recurrent models are a natural choice to address path prediction tasks, where a
trained model is used to generate future expectations from past observations.
When applied to security applications, like predicting the path of pedestrians
for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is
not feasible, since the environment often allows multiple choices. Therefore, a
robust risk assessment has to take all options into account, even if they are
overall not very likely.
  Towards this end, a combination of particle filter sampling strategies and a
LSTM-MDL model is proposed to address a multi-modal path prediction task. The
capabilities and viability of the proposed approach are evaluated on several
synthetic test conditions, yielding the counter-intuitive result that the
simplest approach performs best. Further, the feasibility of the proposed
approach is illustrated on several real world scenes.
