  In the last three decades, human visual attention has been a topic of great
interest in various disciplines. In computer vision, many models have been
proposed to predict the distribution of human fixations on a visual stimulus.
Recently, thanks to the creation of large collections of data, machine learning
algorithms have obtained state-of-the-art performance on the task of saliency
map estimation. On the other hand, computational models of scanpath are much
less studied. Works are often only descriptive or task specific. This is due to
the fact that the scanpath is harder to model because it must include the
description of a dynamic. General purpose computational models are present in
the literature, but are then evaluated in tasks of saliency prediction, losing
therefore information about the dynamics and the behaviour. In addition, two
technical reasons have limited the research. The first reason is the lack of
robust and uniformly used set of metrics to compare the similarity between
scanpath. The second reason is the lack of sufficiently large and varied
scanpath datasets. In this report we want to help in both directions. We
present FixaTons, a large collection of datasets human scanpaths (temporally
ordered sequences of fixations) and saliency maps. It comes along with a
software library for easy data usage, statistics calculation and implementation
of metrics for scanpath and saliency prediction evaluation.
