Abstract Human scanpath represents the sequence of human eye fixations, revealing the dynamic process of saccadic eye movement when a natural scene is freely viewed by humans. It is valuable to have an in-depth understanding of the dynamic visual attention and visual search behavior. In this paper, a deep convolutional saccadic model (DCSM) is proposed to predict human scanpath. The model simultaneously predicts the foveal saliency maps and fixation durations with considering on modeling the inhibition of return, which is a well recognized physiological mechanism to mimic human saccadic behavior. Both the foveal saliency and fixation durations are predicted by convolutional neural networks, which associate the inhibition of return with image content from spatial and temporal aspects. With the proposed DCSM, fixations of a scanpath are sequentially predicted with only a single image as input. Our method is capable of handling the challenges of temporal dependency and spatial association with image content. Experimental results on MIT1003 and FIGRIM datasets demonstrate the effectiveness of our proposed method when compared with state-of-the-art methods.

中文翻译：

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基于深度卷积扫视模型的人体扫描路径预测

摘要 人体扫描路径代表了人眼注视的序列，揭示了人类自由观看自然场景时扫视眼球运动的动态过程。深入了解动态视觉注意力和视觉搜索行为是很有价值的。在本文中，提出了一种深度卷积扫视模型（DCSM）来预测人体扫描路径。该模型同时预测中央凹显着图和注视持续时间，并考虑对返回抑制进行建模，这是一种公认​​的模拟人类扫视行为的生理机制。中央凹显着性和注视持续时间都由卷积神经网络预测，卷积神经网络将返回的抑制与空间和时间方面的图像内容相关联。使用提议的 DCSM，仅使用单个图像作为输入，依次预测扫描路径的注视点。我们的方法能够处理与图像内容的时间依赖性和空间关联的挑战。与最先进的方法相比，MIT1003 和 FIGRIM 数据集的实验结果证明了我们提出的方法的有效性。