  Improving irradiance forecasting is critical to further increase the share of
solar in the energy mix. On a short time scale, fish-eye cameras on the ground
are used to capture cloud displacements causing the local variability of the
electricity production. As most of the solar radiation comes directly from the
Sun, current forecasting approaches use its position in the image as a
reference to interpret the cloud cover dynamics. However, existing Sun tracking
methods rely on external data and a calibration of the camera, which requires
access to the device. To address these limitations, this study introduces an
image-based Sun tracking algorithm to localise the Sun in the image when it is
visible and interpolate its daily trajectory from past observations. We
validate the method on a set of sky images collected over a year at SIRTA's
lab. Experimental results show that the proposed method provides robust smooth
Sun trajectories with a mean absolute error below 1% of the image size.
