Accurate image localization based on google maps street view
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Research and applications on georeferenced multimedia: a survey
Multimedia Tools and Applications
Fast detection of noisy GPS and magnetometer tags in wide-baseline multi-views
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Calibrating a wide-area camera network with non-overlapping views using mobile devices
ACM Transactions on Sensor Networks (TOSN)
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This paper proposes a novel method for estimating the geospatial trajectory of a moving camera. The proposed method uses a set of reference images with known GPS (global positioning system) locations to recover the trajectory of a moving camera using geometric constraints. The proposed method has three main steps. First, scale invariant features transform (SIFT) are detected and matched between the reference images and the video frames to calculate a weighted adjacency matrix (WAM) based on the number of SIFT matches. Second, using the estimated WAM, the maximum matching reference image is selected for the current video frame, which is then used to estimate the relative position (rotation and translation) of the video frame using the fundamental matrix constraint. The relative position is recovered upto a scale factor and a triangulation among the video frame and two reference images is performed to resolve the scale ambiguity. Third, an outlier rejection and trajectory smoothing (using b-spline) post processing step is employed. This is because the estimated camera locations may be noisy due to bad point correspondence or degenerate estimates of fundamental matrices. Results of recovering camera trajectory are reported for real sequences.