Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition

Author:

Leszczuk Mikołaj1ORCID,Janowski Lucjan1ORCID,Nawała Jakub2ORCID,Zhu Jingwen3,Wang Yuding4,Boev Atanas5

Affiliation:

1. AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków, Poland

2. Department of Electrical Electronic Engineering, University of Bristol, Bristol BS8 1QU, UK

3. Department of Computer Science, UMR_6004 Nante Digital Science Laboratory, Nantes University, 44322 Nantes, France

4. Institute of Electronics and Digital Technologies, University of Rennes, 35042 Rennes, France

5. Huawei Technologies Dusseldorf GmbH, 40549 Düsseldorf, Germany

Abstract

In the realm of modern video processing systems, traditional metrics such as the Peak Signal-to-Noise Ratio and Structural Similarity are often insufficient for evaluating videos intended for recognition tasks, like object or license plate recognition. Recognizing the need for specialized assessment in this domain, this study introduces a novel approach tailored to Automatic License Plate Recognition (ALPR). We developed a robust evaluation framework using a dataset with ground truth coordinates for ALPR. This dataset includes video frames captured under various conditions, including occlusions, to facilitate comprehensive model training, testing, and validation. Our methodology simulates quality degradation using a digital camera image acquisition model, representing how luminous flux is transformed into digital images. The model’s performance was evaluated using Video Quality Indicators within an OpenALPR library context. Our findings show that the model achieves a high F-measure score of 0.777, reflecting its effectiveness in assessing video quality for recognition tasks. The proposed model presents a promising avenue for accurate video quality assessment in ALPR tasks, outperforming traditional metrics in typical recognition application scenarios. This underscores the potential of the methodology for broader adoption in video quality analysis for recognition purposes.

Funder

Huawei Innovation Research Program

Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Krakow

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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