  Geo-localizing static objects from street images is challenging but also very
important for road asset mapping and autonomous driving. In this paper we
present a two-stage framework that detects and geolocalizes traffic signs from
low frame rate street videos. Our proposed system uses a modified version of
RetinaNet (GPS-RetinaNet), which predicts a positional offset for each sign
relative to the camera, in addition to performing the standard classification
and bounding box regression. Candidate sign detections from GPS-RetinaNet are
condensed into geolocalized signs by our custom tracker, which consists of a
learned metric network and a variant of the Hungarian Algorithm. Our metric
network estimates the similarity between pairs of detections, then the
Hungarian Algorithm matches detections across images using the similarity
scores provided by the metric network. Our models were trained using an updated
version of the ARTS dataset, which contains 25,544 images and 47.589 sign
annotations ~\cite{arts}. The proposed dataset covers a diverse set of
environments gathered from a broad selection of roads. Each annotaiton contains
a sign class label, its geospatial location, an assembly label, a side of road
indicator, and unique identifiers that aid in the evaluation. This dataset will
support future progress in the field, and the proposed system demonstrates how
to take advantage of some of the unique characteristics of a realistic
geolocalization dataset.
