Metric measurement from street view sequences with simple operator assistance and phase correlation based frame selection

This paper presents a metric measurement approach from sequences of images captured from a moving spherical camera without the need of additional equipment, such as laser scanners or motion detection units. The user assists the algorithms with simple inputs to facilitate the measurement process. The operator initially selects a keyframe that contains the object of interest that is to be measured. Next, a suitable pair is selected for this keyframe, automatically, using a novel phase correlation based approach proposed in this paper. Then, correspondence matching between these two images is performed using scale-invariant feature transform (SIFT) and these features are refined using RANdom SAmple Consensus (RANSAC) and information obtained from the phase correlation stage. As a last step conversion form the image domain to the 3D domain is performed. The user selects two corresponding point pairs in both frames, corresponding to the edges of the distance that is to be measured, and the metric distance between these two points is obtained. During this process, the height information of the camera with respect to the ground is used as basic reference to obtain metric results. Experimental results show that the proposed methods can provide metric measurements with up to 10% error.