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CrossCount A Deep Learning System for Device-free Human Counting using WiFi

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Document pages: 8 pages

Abstract: Counting humans is an essential part of many people-centric applications. Inthis paper, we propose CrossCount: an accurate deep-learning-based human countestimator that uses a single WiFi link to estimate the human count in an areaof interest. The main idea is to depend on the temporal link-blockage patternas a discriminant feature that is more robust to wireless channel noise thanthe signal strength, hence delivering a ubiquitous and accurate human countingsystem. As part of its design, CrossCount addresses a number of deep learningchallenges such as class imbalance and training data augmentation for enhancingthe model generalizability. Implementation and evaluation of CrossCount inmultiple testbeds show that it can achieve a human counting accuracy to withina maximum of 2 persons 100 of the time. This highlights the promise ofCrossCount as a ubiquitous crowd estimator with non-labour-intensive datacollection from off-the-shelf devices.

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