The commercially used automated number plate recognition (ANPR) sys- tems constitute a mature technology which relies on dedicated industrial cam- eras capable of capturing high-quality still images. In contrast, the problem of ANPR from low-quality video sequences has been so far severely under- explored. This thesis proposes a trainable convolutional neural network (CNN) with a novel architecture which can efficiently recognize number plates from low-quality videos of arbitrary length. The proposed network is experimentally shown to outperform several existing approaches dealing with video-sequences, state-of-the-art commercial ANPR system as well as the human ability to recog- nize number plates from low-resolution images. The second contribution of the thesis is a semi-automatic pipeline which was used to create a novel database containing annotated sequences of challenging low-resolution number plate im- ages. The third contribution is a novel CNN based generator of super-resolution number plate images. The generator translates the input low-resolution image into its high-quality counterpart which preserves the structure of the input and depicts the same string which was previously predicted from a video-sequence. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:387247 |
| Date | January 2018 |
| Creators | Vašek, Vojtěch |
| Contributors | Franc, Vojtěch, Šikudová, Elena |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
Page generated in 0.002 seconds