  Understanding human visual attention and saliency is an integral part of
vision research. In this context, there is an ever-present need for fresh and
diverse benchmark datasets, particularly for insight into special use cases
like crowded scenes. We contribute to this end by: (1) reviewing the dynamics
behind saliency and crowds. (2) using eye tracking to create a dynamic human
eye fixation dataset over a new set of crowd videos gathered from the Internet.
The videos are annotated into three distinct density levels. (3) Finally, we
evaluate state-of-the-art saliency models on our dataset to identify possible
improvements for the design and creation of a more robust saliency model.
