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Can knowing where you are assist in perceiving objects in your surroundings,especially under adverse weather and lighting conditions? In this work weinvestigate whether a prior map can be leveraged to aid in the detection ofdynamic objects in a scene without the need for a 3D map or pixel-levelmap-query correspondences. We contribute an algorithm which refines an initialset of candidate object detections and produces a refined subset of highlyaccurate detections using a prior map. We begin by using visual placerecognition (VPR) to retrieve a reference map image for a given query image,then use a binary classification neural network that compares the query andmapping image regions to validate the query detection. Once our classificationnetwork is trained, on approximately 1000 query-map image pairs, it is able toimprove the performance of vehicle detection when combined with an existingoff-the-shelf vehicle detector. We demonstrate our approach using standarddatasets across two cities (Oxford and Zurich) under different settings oftrain-test separation of map-query traverse pairs. We further emphasize theperformance gains of our approach against alternative design choices and showthat VPR suffices for the task, eliminating the need for precise ground truthlocalization.

DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions