A Study of Human Gaze Behavior During Visual Crowd Counting

Raji Annadi¹, Yupei Chen², Viresh Ranjan¹, Dimitris Samaras¹, Gregory Zelinsky², Minh Hoai¹

¹Department of Computer Science, ²Department of Psychology

Stony Brook University, Stony Brook, NY 11790

Abstract

We study how humans allocate their attention during visual crowdcounting. 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.

Dataset

This dataset contains human gaze data of 10 subjects performing visual crowd counting tasks on 30 images. All the data and code can be download from here. Please refer to the README.html file for the data structure.

Paper and Citation

A Study of Human Gaze Behavior During Visual Crowd Counting.
R. Annadi, Y. Chen, V. Ranjan, D. Samaras, G. Zelinsky, M. Hoai (2020)
arXiv:2009.06502.
Paper BibTex

Examples

Figure 1: A sparse crowd image and fixation density maps from 10 subjects.

Figure 2: A dense crowd image and fixation density maps from 10 subjects.

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