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Switching Convolutional Neural Network for Crowd Counting
We propose a novel crowd counting model that maps a given crowd scene to...
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CrossCount: A Deep Learning System for Device-free Human Counting using WiFi
Counting humans is an essential part of many people-centric applications...
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Fooling the Crowd with Deep Learning-based Methods
Modern, state-of-the-art deep learning approaches yield human like perfo...
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Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach
This paper presents a crowd monitoring system based on the passive detec...
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Peek-a-boo, I Can See You, Forger: Influences of Human Demographics, Brand Familiarity and Security Backgrounds on Homograph Recognition
Homograph attack is a way that attackers deceive victims about which dom...
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A Strong Baseline for Crowd Counting and Unsupervised People Localization
In this paper, we explore a strong baseline for crowd counting and an un...
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Crowd Counting Through Walls Using WiFi
Counting the number of people inside a building, from outside and withou...
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A Study of Human Gaze Behavior During Visual Crowd Counting
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/ cvl/projects/crowd_counting_gaze/
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