Moussa D, Maier A, Spruck A, Seiler J, Rieß C (2022)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2022
Publisher: IEEE
Pages Range: 406-410
Conference Proceedings Title: 2022 IEEE International Conference on Image Processing (ICIP)
Event location: Bordeaux, France
ISBN: 978-1-6654-9620-9
URI: https://faui1-files.cs.fau.de/public/publications/mmsec/2022-Moussa-FLPR.pdf
DOI: 10.1109/ICIP46576.2022.9897178
Open Access Link: https://faui1-files.cs.fau.de/public/publications/mmsec/2022-Moussa-FLPR.pdf
Forensic license plate recognition (FLPR) remains an open challenge in legal contexts such as criminal investigations, where unreadable license plates (LPs) need to be deciphered from highly compressed and/or low resolution footage, e.g., from surveillance cameras. In this work, we propose a side-informed Transformer architecture that embeds knowledge on the input compression level to improve recognition under strong compression. We show the effectiveness of Transformers for license plate recognition (LPR) on a low-quality real-world dataset. We also provide a synthetic dataset that includes strongly degraded, illegible LP images and analyze the impact of knowledge embedding on it. The network outperforms existing FLPR methods and standard state-of-the art image recognition models while requiring less parameters. For the severest degraded
images, we can improve recognition by up to 8.9 percent points.
APA:
Moussa, D., Maier, A., Spruck, A., Seiler, J., & Rieß, C. (2022). Forensic License Plate Recognition with Compression-Informed Transformers. In IEEE (Eds.), 2022 IEEE International Conference on Image Processing (ICIP) (pp. 406-410). Bordeaux, France, FR: IEEE.
MLA:
Moussa, Denise, et al. "Forensic License Plate Recognition with Compression-Informed Transformers." Proceedings of the IEEE International Conference on Image Processing, Bordeaux, France Ed. IEEE, IEEE, 2022. 406-410.
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