Accurate Image Localization Based on Google Maps Street View

Finding an image’s exact GPS location is a challenging computer vision problem that has many real-world applications. In this paper, we address the problem of finding the GPS location of images with an accuracy which is comparable to hand-held GPS devices.We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree. In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors. Then, a smoothing step with an associated voting scheme is utilized; this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A parameter called

Confidence of Localization

which is based on the Kurtosis of the distribution of votes is defined to determine how reliable the localization of a particular image is. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. First, each image is localized individually; then, the rest of the images in the group are matched against images in the neighboring area of the found first match. The final location is determined based on the

Confidence of Localization

parameter. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.