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CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting
Crowd counting problem that counts the number of people in an image has ...
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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 attent...
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Fully Convolutional Crowd Counting On Highly Congested Scenes
In this paper we advance the state-of-the-art for crowd counting in high...
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Adaptive Scenario Discovery for Crowd Counting
Crowd counting, i.e., estimation number of pedestrian in crowd images, i...
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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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Privacy Preserving Count Statistics
The ability to preserve user privacy and anonymity is important. One of ...
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Dense Crowds Detection and Counting with a Lightweight Architecture
In the context of crowd counting, most of the works have focused on impr...
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Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach
This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm—as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.
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