Abstract
To facilitate the research in visual attention analysis, we design and establish a new task-driven eye tracking dataset of 47 subjects. Inspired by psychological findings that human visual behavior is tightly dependent on the executed tasks, we carefully design specific tasks in accordance with the contents of 111 images covering various semantic categories, such as text, facial expression, texture, pose, and gaze. It results in a dataset of 111 fixation density maps and over 5,000 scanpaths. Moreover, we provide baseline results of thirteen state-of-the-art saliency models. Furthermore, we hold discussions on important clues on how tasks and image contents influence human visual behavior. This task-driven eye tracking dataset with the fixation density maps and scanpaths will be made publicly available.
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Xu, Y., Hong, X., He, Q., Zhao, G., Pietikäinen, M. (2015). A Task-Driven Eye Tracking Dataset for Visual Attention Analysis. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_55
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DOI: https://doi.org/10.1007/978-3-319-25903-1_55
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