Modern aerial imaging platforms provide wide-area motion imagery (WAMI) at high spatial and moderate temporal resolutions making feasible a range of new applications. We consider the dual tasks of registering WAMI frames to geo-referenced vector road-maps and tracking vehicles through the progression of WAMI frames. We present a novel algorithm that performs these tasks jointly and offers improvements in both by exploiting the synergy between the tasks. Tracking for the large number of vehicles seen in urban-area WAMI is improved by auxiliary information that registration to the vector road-map provides by localizing roads within the scene. Similarly, registration of the WAMI frames to the vector map is improved by formulating the registration as a chamfer minimization between the vehicular trajectories and the road network, an approach that resolves challenges for registration posed by the fundamentally different data modalities between the aerial images and the vector road maps. Results obtained over our test datasets show the effectiveness of the proposed joint methodology. For both road network alignment and vehicle tracking, the proposed method offers a very significant improvement over available alternatives: the proposed approach yields better numerical metrics for quantification of registration accuracy and fewer false identification switches for tracked vehicles.