ArTS Archivio della ricerca di Trieste
ArTS è il sistema di gestione integrata dei dati della ricerca adottato dall' Università degli Studi di Trieste
EnglishOne task often encountered in surveillance videos is the recognition of a target—e.g. the license plate of a vehicle. Often, the quality of a single video frame does not permit a reliable recognition. If multiple frames are available, it is possible to combine them in order to generate a single image with lower noise (frame averaging) and/or higher resolution (super-resolution). In order for these techniques to work, it is necessary to accurately estimate the motion of the object of interest in the recorded footage. In this paper, we introduce a method capable of accurately computing the perspective transformation that describes the motion of a planar object. The method minimizes the squared distance between the transformed image and a reference, computed over a user-defined region of interest, and uses the partial derivatives in order to significantly speed up the computation. This approach is inspired by the well known Kanade–Lucas–Tomasi feature tracker.
| Titolo: | Perspective registration and multi-frame super-resolution of license plates in surveillance videos | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Stato di pubblicazione: | Pubblicato | |
| Rivista: | ||
| Abstract: | One task often encountered in surveillance videos is the recognition of a target—e.g. the license plate of a vehicle. Often, the quality of a single video frame does not permit a reliable recognition. If multiple frames are available, it is possible to combine them in order to generate a single image with lower noise (frame averaging) and/or higher resolution (super-resolution). In order for these techniques to work, it is necessary to accurately estimate the motion of the object of interest in the recorded footage. In this paper, we introduce a method capable of accurately computing the perspective transformation that describes the motion of a planar object. The method minimizes the squared distance between the transformed image and a reference, computed over a user-defined region of interest, and uses the partial derivatives in order to significantly speed up the computation. This approach is inspired by the well known Kanade–Lucas–Tomasi feature tracker. | |
| Handle: | http://hdl.handle.net/11368/2979717 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.fsidi.2020.301087 | |
| URL: | http://www.sciencedirect.com/science/article/pii/S2666281720303899 | |
| Appare nelle tipologie: | 1.1 Articolo in Rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2020_fsi-di.pdf | fsi-di 2020 | Documento in Versione Editoriale | Copyright Editore | Administrator Richiedi una copia |
