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In the last three decades, human visual attention has been a topic of greatinterest in various disciplines. In computer vision, many models have beenproposed to predict the distribution of human fixations on a visual stimulus.Recently, thanks to the creation of large collections of data, machine learningalgorithms have obtained state-of-the-art performance on the task of saliencymap estimation. On the other hand, computational models of scanpath are muchless studied. Works are often only descriptive or task specific. This is due tothe fact that the scanpath is harder to model because it must include thedescription of a dynamic. General purpose computational models are present inthe literature, but are then evaluated in tasks of saliency prediction, losingtherefore information about the dynamics and the behaviour. In addition, twotechnical reasons have limited the research. The first reason is the lack ofrobust and uniformly used set of metrics to compare the similarity betweenscanpath. The second reason is the lack of sufficiently large and variedscanpath datasets. In this report we want to help in both directions. Wepresent FixaTons, a large collection of datasets human scanpaths (temporallyordered sequences of fixations) and saliency maps. It comes along with asoftware library for easy data usage, statistics calculation and implementationof metrics for scanpath and saliency prediction evaluation.

FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity