Reliability Scoring for the Recognition of Degraded License Plates

Maier A, Moussa D, Spruck A, Seiler J, Rieß C (2022)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Event location: Madrid

URI: https://faui1-files.cs.fau.de/public/publications/mmsec/2022-Maier-AVSS.pdf

DOI: 10.1109/AVSS56176.2022.9959390

Abstract

Criminal investigations oftentimes need the identification of license plates of escape vehicles.
The vehicles may be recorded by low-quality cameras in the wild. Their license plates may be
unreadable for police officers. Recent efforts aim to use machine learning to forensically decipher
license plates from such low-quality images. These methods operate near the information-theoretic
limit of recognition and hence show quite high error rates. Unfortunately, it is unclear when such
prediction errors occur, which makes it difficult to use these methods in practice. In this work, we
propose a Bayesian Neural Network to inherently incorporate a reliability measure into the classifier.
We additionally propose to integrate multiple estimations with an entropy weight to further improve
the reliability. Our experiments show that this uncertainty metric dramatically reduces the number
of false predictions while preserving most of the true predictions.

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How to cite

APA:

Maier, A., Moussa, D., Spruck, A., Seiler, J., & Rieß, C. (2022). Reliability Scoring for the Recognition of Degraded License Plates. In Proceedings of the 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Madrid, ES.

MLA:

Maier, Anatol, et al. "Reliability Scoring for the Recognition of Degraded License Plates." Proceedings of the 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Madrid 2022.

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