Identifying truck correspondence in multi-frame imagery

This paper develops a model for automated matching of vehicles between aerial photos. We exploit the overlap from frame-to-frame in a sequence of aerial photos. The problem, in essence, is to determine if vehicles A, B, C, … in one photo, match vehicles X, Y, and Z in a temporally lagged second photo. The method is specifically applied to large trucks (tractor-trailer combinations). If a match can be established, the trucks can be joined to form trajectories. The need for an automated algorithm is pressing in view of the likely stream of data from various new aerial platforms (video, tethered balloons, unmanned airborne data collection systems) and the development of tools to automate the extraction of trucks from these images. In the first step, manual air photo interpretation is used to determine the exact match of the real trucks. Digitized points in the images represent observations of unique trucks, each of which is seen a number of times. A distance matrix is computed between all pairs of points (trucks) on the network. Then, a linear program is used to select matching trucks. A total of 10 pairs of photos incorporating 85 potential matches are described. Reasonable results are found, especially for implied speed estimates, which can be compared to the actual speeds from the true matches. Challenges for future extensions to more complex situations are explained.