DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place
  Recognition under Adverse Conditions
Stephen Hausler Sourav Garg Punarjay Chakravarty Shubham Shrivastava Ankit Vora Michael Milford
Can knowing where you are assist in perceiving objects in your surroundings,
especially under adverse weather and lighting conditions? In this work we
investigate whether a prior map can be leveraged to aid in the detection of
dynamic objects in a scene without the need for a 3D map or pixel-level
map-query correspondences. We contribute an algorithm which refines an initial
set of candidate object detections and produces a refined subset of highly
accurate detections using a prior map. We begin by using visual place
recognition (VPR) to retrieve a reference map image for a given query image,
then use a binary classification neural network that compares the query and
mapping image regions to validate the query detection. Once our classification
network is trained, on approximately 1000 query-map image pairs, it is able to
improve the performance of vehicle detection when combined with an existing
off-the-shelf vehicle detector. We demonstrate our approach using standard
datasets across two cities (Oxford and Zurich) under different settings of
train-test separation of map-query traverse pairs. We further emphasize the
performance gains of our approach against alternative design choices and show
that VPR suffices for the task, eliminating the need for precise ground truth
localization.