  Eye-tracking has potential to provide rich behavioral data about human
cognition in ecologically valid environments. However, analyzing this rich data
is often challenging. Most automated analyses are specific to simplistic
artificial visual stimuli with well-separated, static regions of interest,
while most analyses in the context of complex visual stimuli, such as most
natural scenes, rely on laborious and time-consuming manual annotation. This
paper studies using computer vision tools for "attention decoding", the task of
assessing the locus of a participant's overt visual attention over time. We
provide a publicly available Multiple Object Eye-Tracking (MOET) dataset,
consisting of gaze data from participants tracking specific objects, annotated
with labels and bounding boxes, in crowded real-world videos, for training and
evaluating attention decoding algorithms. We also propose two end-to-end deep
learning models for attention decoding and compare these to state-of-the-art
heuristic methods.
