  Reflections in natural images commonly cause false positives in automated
detection systems. These false positives can lead to significant impairment of
accuracy in the tasks of detection, counting and segmentation. Here, inspired
by the recent panoptic approach to segmentation, we show how fusing instance
and semantic segmentation can automatically identify reflection false
positives, without explicitly needing to have the reflective regions labelled.
We explore in detail how state of the art two-stage detectors suffer a loss of
broader contextual features, and hence are unable to learn to ignore these
reflections. We then present an approach to fuse instance and semantic
segmentations for this application, and subsequently show how this reduces
false positive detections in a real world surveillance data with a large number
of reflective surfaces. This demonstrates how panoptic segmentation and related
work, despite being in its infancy, can already be useful in real world
computer vision problems.
