We present a novel methodology for accurately registering a vector road map to wide area motion imagery (WAMI) gathered from an oblique perspective by exploiting the local motion associated with vehicular movements. Specifically, we identify and compensate for global motion from frame-to-frame in the WAMI which then allows ready detection of local motion that corresponds strongly with the locations of moving vehicles along the roads. Minimization of the chamfer distance between these identified locations and the network of road lines identified in the vector road map provides an accurate alignment between the vector road map and the WAMI image frame under consideration. The methodology provides a significant improvement over the approximate geo-tagging provided by on-board sensors and effectively side-steps the challenge of matching features between the completely different data modalities and viewpoints for the vector road map and the captured WAMI frames. Results over a test WAMI dataset indicate the effectiveness of the proposed methodology: both visual comparison and numerical metrics for the alignment accuracy are significantly better for the proposed method as compared with existing alternatives.