Papers › CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos
CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos
Memoona Tahira, Sobas Mehboob, Anis U. Rahman, Omar Arif
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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