For the purpose of extended object tracking in multiple hypothesis tracking algorithms such as the Gaussian mixture probability hypothesis density filter (GMPHD), we develop an approach for the combination of different contour estimates. The developed approach works for tracking algorithms that represent target shapes using contour functions to describe the target shape as the distance of the contour to a reference point over the angle. In a heterogeneous multiple sensor setup, the individual sensors' measurements lead to different extent estimates due to their individual measurement principles. Thus a straight forward use of the extended object state in the traditional merging algorithm either results in unexpected shapes, or tracks cannot be merged due to the differing shape of the objects. Our merging procedure explicitly takes the extent estimates into account by using a merging function. The choice of the merging function provides the means to reach objectives such as a conservative or a generous extent estimate. We evaluate the approach using simulated multisensor data in a GMPHD filter. Compared to the traditional merging method, our approach results in better shape estimates.