Due to the progress in communication technologies and the ever-shrinking form-factors of electronic devices, sensors are becoming increasingly ubiquitous in daily life. In smart-home applications especially, detecting the number of humans present and determining their positions allows for intelligent controls of heating, ventilation, lighting, and entertainment systems, potentially saving significant amounts of energy and CO2 emissions in the process. However, while recent progress through deep learning in computer vision enables cameras to detect human targets accurately, privacy concerns and their dependency on good sight conditions prevent their adoption. Contrary to that, radar sensors are privacy-preserving, work independent of sight conditions, and allow for the estimation of range, velocity, and angle information of targets. This work explores the use of millimeter-wave frequency continuous wave radar sensors for estimating the number of human targets and detecting their positions in indoor environments. To this date, most methods in literature on radar-based tracking, detection, and counting, focus on automotive applications and are mostly based on traditional signal processing-based methods. The work presented in this thesis shows that deep learning enables a low-cost radar sensor to succeed in said tasks in complex multi-target indoor environments, where signal-processing-based methods reach their limits due to multi-path reflections, clutter, and ghost targets. To this end, new training schemes, network architectures, and loss functions were developed to enable performance gains similar to what was seen with the advent of deep learning in computer vision. ; Aufgrund der Fortschritte in der Kommunikationstechnologie und der immer kleiner werdenden Formfaktoren elektronischer Geräte werden Sensoren im täglichen Leben zunehmend allgegenwärtig. Vor allem bei Smart-Home-Anwendungen ermöglicht die Erkennung der Anzahl der anwesenden Personen und die Vorhersage ihrer Position eine intelligente Steuerung von ...
Deep Learning-based Resilient People Counting and Localization using a 60 GHz FMCW Radar ; Deep Learning-basiertes robustes Zählen und Lokalisieren von Menschen mit einem 60 GHz FMCW Radar
2023-01-01
Theses
Electronic Resource
English
DDC: | 629 |
British Library Conference Proceedings | 1993
|Tema Archive | 1992
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