  Instance segmentation of images is an important tool for automated scene
understanding. Neural networks are usually trained to optimize their overall
performance in terms of accuracy. Meanwhile, in applications such as automated
driving, an overlooked pedestrian seems more harmful than a falsely detected
one. In this work, we present a false negative detection method for image
sequences based on inconsistencies in time series of tracked instances given
the availability of image sequences in online applications. As the number of
instances can be greatly increased by this algorithm, we apply a false positive
pruning using uncertainty estimates aggregated over instances. To this end,
instance-wise metrics are constructed which characterize uncertainty and
geometry of a given instance or are predicated on depth estimation. The
proposed method serves as a post-processing step applicable to any neural
network that can also be trained on single frames only. In our tests, we obtain
an improved trade-off between false negative and false positive instances by
our fused detection approach in comparison to the use of an ordinary score
value provided by the instance segmentation network during inference.
