FixaTons：人体注视数据集的集合和扫描路径相似性的分析
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity
摘要：
在过去的三十年里，人类视觉注意一直是各个学科非常感兴趣的话题。在计算机视觉中，已经提出了许多模型来预测人类对视觉刺激的注视的分布。最近，由于创建了大量数据，机器学习算法在显著图估计任务上获得了最先进的性能。另一方面，对扫描路径的计算模型研究较少。作品往往只是描述性的或特定的任务。这是因为扫描路径更难建模，因为它必须包括动态的描述。通用计算模型存在于文献中，但随后在显著性预测任务中进行评估，因此丢失了关于动态和行为的信息。此外，两个技术原因限制了研究。第一个原因是缺乏鲁棒的和统一使用的度量来比较扫描路径之间的相似性。第二个原因是缺乏足够大和多样的扫描路径数据集。在这份报告中，我们希望在两个方向上提供帮助。我们提出了FixaTons，一个大型的数据集人类扫描路径（时间顺序的固定序列）和显着性图。它附带了一个软件库，便于数据使用，统计计算和实施扫描路径和显着性预测评估的指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
