People Counting from mmWave Radar Point Clouds with Graph Neural Networks

Bachelor thesis (2024)

Authors

B.A. Bakos Electrical Engineering, Mathematics and Computer Science

Contributors

G. Vaidya Networked Systems - EEMCS (mentor)
Marco Zuniga Networked Systems - EEMCS (mentor)
M. Weinmann Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
People counting MmWave radar Graph Neural Network
More Info
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Published Date

26-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Crowd-control is an emerging problem in urban areas and cameras are commonly seen as the solution, however, there are major concerns regarding privacy. To overcome these issues, while still maintaining the ability to keep track of people, mmWave sensors can be utilized instead, but this introduces new challenges when it comes to people counting. They work by recording the data as point clouds, making it difficult to determine the real number of people when they are occluded. To address this challenge, we combine the spatial and temporal information of the point clouds into graphs, and explore the possibilities of Graph Neural Networks. Our system classifies up to 5 people and bikes with an accuracy of 80.47%. This accuracy is 2.53% worse than the state of the art people counting system for mmWave radar point clouds.