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arXiv · 1910.02618

CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos

Memoona Tahira·Sobas Mehboob·Anis U. Rahman·Omar Arif
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Abstract

Understanding human visual attention and saliency is an integral part of vision research. In this context, there is an ever-present need for fresh and diverse benchmark datasets, particularly for insight into special use cases like crowded scenes. We contribute to this end by: (1) reviewing the dynamics behind saliency and crowds. (2) using eye tracking to create a dynamic human eye fixation dataset over a new set of crowd videos gathered from the Internet. The videos are annotated into three distinct density levels. (3) Finally, we evaluate state-of-the-art saliency models on our dataset to identify possible improvements for the design and creation of a more robust saliency model.

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Memoona Tahira, Sobas Mehboob, Anis U. Rahman, Omar Arif. 2019-10-07. CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos. https://arxiv.org/abs/1910.02618

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