  Over the past few decades, a significant rise of camera-based applications
for traffic monitoring has occurred. Governments and local administrations are
increasingly relying on the data collected from these cameras to enhance road
safety and optimize traffic conditions. However, for effective data
utilization, it is imperative to ensure accurate and automated calibration of
the involved cameras. This paper proposes a novel approach to address this
challenge by leveraging the topological structure of intersections. We propose
a framework involving the generation of a set of synthetic intersection
viewpoint images from a bird's-eye-view image, framed as a graph of virtual
cameras to model these images. Using the capabilities of Graph Neural Networks,
we effectively learn the relationships within this graph, thereby facilitating
the estimation of a homography matrix. This estimation leverages the
neighbourhood representation for any real-world camera and is enhanced by
exploiting multiple images instead of a single match. In turn, the homography
matrix allows the retrieval of extrinsic calibration parameters. As a result,
the proposed framework demonstrates superior performance on both synthetic
datasets and real-world cameras, setting a new state-of-the-art benchmark.
