Szczegóły publikacji
Opis bibliograficzny
Estimation of absolute distance and height of people based on monocular view and deep neural networks for edge devices operating in the visible and thermal spectra / Jan Gąsienica-Józkowy, Bogusław CYGANEK, Mateusz KNAPIK, Szymon Głogowski, Łukasz Przebinda // W: FedCSIS 2023 [Dokument elektroniczny] : proceedings of the 18th conference on Computer Science and Intelligence Systems : September 17–20, 2023, Warsaw, Poland / eds. Maria Ganzha, [et al.]. — Wersja do Windows. — Dane tekstowe. — Warszawa : Polskie Towarzystwo Informatyczne, cop. 2023. — (Annals of Computer Science and Information Systems ; ISSN 2300-5963 ; Vol. 35). — Dod.: ART: ISBN 978-83-969601-0-8, USB: ISBN 978-83-967447-9-1. — e-ISBN: 978-83-967447-8-4. — S. 503–511. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://annals-csis.org/proceedings/2023/drp/pdf/3560.pdf [2023-10-06]. — Bibliogr. s. 510–511, Abstr. — J. Gąsienica-Józkowy, B. Cyganek, M. Knapik - afiliacja: Faculty of Computer Science, Electronics and Telecommunication ; J. Gąsienica-Józkowy, B. Cyganek, M. Knapik - dod. afiliacja: MyLED Inc., Kraków, Poland
Autorzy (5)
- AGHGąsienica-Józkowy Jan
- AGHCyganek Bogusław
- AGHKnapik Mateusz
- Głogowski Szymon
- Przebinda Łukasz
Dane bibliometryczne
| ID BaDAP | 149263 |
|---|---|
| Data dodania do BaDAP | 2023-11-10 |
| DOI | 10.15439/2023F3560 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | Conference on Computer Science and Intelligence Systems 2023 |
| Czasopismo/seria | Annals of Computer Science and Information Systems |
Abstract
Accurate estimation of absolute distance and height of objects in open area conditions is a significant challenge. In this paper, we address these problems and we propose a novel approach that combines classical computer vision algorithms with modern neural network-based solutions. Our method integrates object detection, monocular depth estimation, and homographybased mapping to achieve precise and efficient estimations of absolute height and distance. The solution is implemented on the edge device, which enables real-time data processing using both visual and thermography data sources. Experimental evaluation on a height estimation dataset prepared by us demonstrates an accuracy of 97.06% and validates the effectiveness of our approach.
