  In this paper, we describe our study on how humans allocate their attention
during visual crowd counting. Using an eye tracker, we collect gaze behavior of
human participants who are tasked with counting the number of people in crowd
images. Analyzing the collected gaze behavior of ten human participants on
thirty crowd images, we observe some common approaches for visual counting. For
an image of a small crowd, the approach is to enumerate over all people or
groups of people in the crowd, and this explains the high level of similarity
between the fixation density maps of different human participants. For an image
of a large crowd, our participants tend to focus on one section of the image,
count the number of people in that section, and then extrapolate to the other
sections. In terms of count accuracy, our human participants are not as good at
the counting task, compared to the performance of the current state-of-the-art
computer algorithms. Interestingly, there is a tendency to under count the
number of people in all crowd images. Gaze behavior data and images can be
downloaded from
https://www3.cs.stonybrook.edu/~minhhoai/projects/crowd_counting_gaze/.
