  In tasks such as tracking, time-series data inevitably carry missing
observations. While traditional tracking approaches can handle missing
observations, recurrent neural networks (RNNs) are designed to receive input
data in every step. Furthermore, current solutions for RNNs, like omitting the
missing data or data imputation, are not sufficient to account for the
resulting increased uncertainty. Towards this end, this paper introduces an
RNN-based approach that provides a full temporal filtering cycle for motion
state estimation. The Kalman filter inspired approach, enables to deal with
missing observations and outliers. For providing a full temporal filtering
cycle, a basic RNN is extended to take observations and the associated belief
about its accuracy into account for updating the current state. An RNN
prediction model, which generates a parametrized distribution to capture the
predicted states, is combined with an RNN update model, which relies on the
prediction model output and the current observation. By providing the model
with masking information, binary-encoded missing events, the model can overcome
limitations of standard techniques for dealing with missing input values. The
model abilities are demonstrated on synthetic data reflecting prototypical
pedestrian tracking scenarios.
