Schroth, Christian A. ; Eckrich, Christian ; Fabian, Stefan ; Stryk, Oskar von ; Zoubir, Abdelhak M. ; Muma, Michael (2023)
Multi-Person Localization and Vital Sign Estimation Radar Dataset.
doi: 10.21227/4bzd-jm32
Forschungsdaten, Bibliographie
Kurzbeschreibung (Abstract)
The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information.
This dataset provides the possibility to develop algorithms for, e.g., radar-based (through-wall) multi-person detection, localization, 3D direction-of-arrival estimation, breathing frequency estimation or heart beat estimation. The challenging dataset was collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included.
| Typ des Eintrags: | Forschungsdaten |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Schroth, Christian A. ; Eckrich, Christian ; Fabian, Stefan ; Stryk, Oskar von ; Zoubir, Abdelhak M. ; Muma, Michael |
| Art des Eintrags: | Bibliographie |
| Titel: | Multi-Person Localization and Vital Sign Estimation Radar Dataset |
| Sprache: | Englisch |
| Publikationsjahr: | 26 Mai 2023 |
| Ort: | Darmstadt |
| Verlag: | IEEE |
| Kollation: | 7 Seiten |
| DOI: | 10.21227/4bzd-jm32 |
| URL / URN: | https://ieee-dataport.org/open-access/multi-person-localizat... |
| Kurzbeschreibung (Abstract): | The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This dataset provides the possibility to develop algorithms for, e.g., radar-based (through-wall) multi-person detection, localization, 3D direction-of-arrival estimation, breathing frequency estimation or heart beat estimation. The challenging dataset was collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included. |
| Freie Schlagworte: | emergenCITY_CPS, emergenCITY |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
| Hinterlegungsdatum: | 02 Jun 2023 09:41 |
| Letzte Änderung: | 07 Jun 2023 09:33 |
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