  Saliency maps are used to understand human attention and visual fixation.
However, while very well established for static images, there is no general
agreement on how to compute a saliency map of dynamic scenes. In this paper we
propose a mathematically rigorous approach to this prob- lem, including static
saliency maps of each video frame for the calculation of the optical flow.
Taking into account static saliency maps for calculating the optical flow
allows for overcoming the aperture problem. Our ap- proach is able to explain
human fixation behavior in situations which pose challenges to standard
approaches, such as when a fixated object disappears behind an occlusion and
reappears after several frames. In addition, we quantitatively compare our
model against alternative solutions using a large eye tracking data set.
Together, our results suggest that assessing optical flow information across a
series of saliency maps gives a highly accurate and useful account of human
overt attention in dynamic scenes.
