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.
@article{arxiv.1910.02618,
title = {CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos},
author = {Memoona Tahira and Sobas Mehboob and Anis U. Rahman and Omar Arif},
journal= {arXiv preprint arXiv:1910.02618},
year = {2019}
}