A Study of Human Gaze Behavior During Visual Crowd Counting

TL;DR

This study investigates how humans allocate visual attention during crowd counting using eye tracking, revealing common strategies and highlighting the differences in accuracy compared to computer algorithms.

Contribution

It provides detailed analysis of human gaze behavior during crowd counting and introduces a dataset of gaze data and images for further research.

Findings

Humans tend to enumerate or focus on sections depending on crowd size.

Participants generally undercount compared to computer algorithms.

Gaze patterns are similar for small crowds but differ for large crowds.

Abstract

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/.