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Open Access Object-based and multi-frame motion information predict human eye movement patterns during video viewing

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Compared to low-level saliency, higher-level information better predicts human eye movement in static images. In the current study, we tested how both types of information predict eye movements while observers view videos. We generated multiple eye movement prediction maps based on low-level saliency features, as well as higher-level information that requires cognition, and therefore cannot be interpreted with only bottom-up processes. We investigated eye movement patterns to both static and dynamic features that contained either lowor higher-level information. We found that higher-level object-based and multi-frame motion information better predict human eye movement patterns than static saliency and two-frame motion information, and higher-level static and dynamic features provide equally good predictions. The results suggest that object-based processes and temporal integration of multiple video frames are essential to guide human eye movements during video viewing.
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Keywords: Dynamic Features; Eye Movements; Object Recognition

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Object-based and multi-frame motion information predict human eye movement patterns during video viewing".

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