Publication: A Traffic-sign recognition IoT-based Ap...
Master data
Title: | A Traffic-sign recognition IoT-based Application |
Subtitle: | |
Abstract: | International data corporation predicts that 21.5 billion connected Internet of Things (IoT) devices will generate 55% of all data by 2025. Nowadays, camera sensors can be embedded in most devices. Therefore, we designed an application to receive a video stream from a camera sensor and perform the video processing. First our designed application pre-processes the sensed data by two high-quality video encoding and framing frameworks. Afterward, we apply the machine learning (ML) model based on the low and high training accuracies. Because the user devices cannot often perform high-load machine learning training operations, we consider the ML inference operation acting as a lightweight trained ML model. At the end, the processed data is packaged for the consumer such as the driver of a car. |
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Publication type: | Other publication (Authorship) |
Publication date: | 01.06.2022 (Online) |
Published by: |
HiPEAC Info Nr. 66
HiPEAC Info Nr. 66
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to publication |
Title of the series: | - |
Volume number: | - |
Issue: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 43 - 43 |
Versionen
Keine Version vorhanden |
Publication date: | 01.06.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | - |
Homepage: | https://www.hipeac.net/magazine/7162.pdf |
Open access |
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Authors
Narges Mehran (internal) |
Dragi Kimovski (internal) |
Zahra Najafabadi Samani (internal) |
Radu Aurel Prodan (internal) |
Assignment
Organisation | Address | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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