City Scale Geo-Spatial Trajectory Estimation of a Moving Camera

Abstract

This paper presents a novel method for estimating the geospatial trajectory of a moving camera with unknown intrinsic parameters, in a city-scale urban environment. The proposed method is based on a three step process that includes: 1) finding the best visual matches of individual images to a dataset of geo-referenced street view images, 2) Bayesian tracking to estimate the frame localization and its temporal evolution, and 3) a trajectory reconstruction algorithm to eliminate inconsistent estimations. As a result of matching features in query image with the features in the reference geo-taged images, in the first step, we obtain a distribution of geolocated votes of matching features which is interpreted as the likelihood of the location (latitude and longitude) given the current observation. In the second step, Bayesian tracking framework is used to estimate the temporal evolution of frame geolocalization based on the previous state probabilities and current likelihood. Finally, once a trajectory is estimated, we perform a Minimum Spanning Trees (MST) based trajectory reconstruction algorithm to eliminate trajectory loops or noisy estimations. The proposed method was tested on sixty minutes of video, which included footage downloaded from YouTube and footage captured by random users in Orlando and Pittsburgh.