  Object permanence is the concept that objects do not suddenly disappear in
the physical world. Humans understand this concept at young ages and know that
another person is still there, even though it is temporarily occluded. Neural
networks currently often struggle with this challenge. Thus, we introduce
explicit object permanence into two stage detection approaches drawing
inspiration from particle filters. At the core, our detector uses the
predictions of previous frames as additional proposals for the current one at
inference time. Experiments confirm the feedback loop improving detection
performance by a up to 10.3 mAP with little computational overhead.
  Our approach is suited to extend two-stage detectors for stabilized and
reliable detections even under heavy occlusion. Additionally, the ability to
apply our method without retraining an existing model promises wide application
in real-world tasks.
