Optoelectronic and Environmental Factors Affecting the Accuracy of Crowd-Sourced Vehicle-Mounted License Plate Recognition
License plate recognition (LPR) technology has been used to combat vehicle-related crime in urban areas in many developed contexts. However, commercially available LPR systems are expensive and not feasible for large scale adoption in developing countries. The development of a low-cost crowd-sourced solution requires an informed approach to the selection of an appropriate camera, as well as a realistic understanding of the system's performance under various environmental conditions. This work investigates the effect of optoelectronic and environmental factors on the ability of a vehicle-mounted LPR system to correctly identify license plates, specifically for a mass-deployment crowd-sourced scenario. A theoretical LPR camera model was developed to estimate the effect of different cameras, while the effects of motion, orientation and lighting were evaluated in a series of experimental tests. The most influential optoelectronic factors were shown to be focus, focal length and image sensor resolution. Furthermore, recognition was impaired during high-speed turn maneuvers, angling of license plates away from the camera and certain night-time conditions. The optoelectronic model proved useful for the selection of a cost-effective camera for use in an open-source LPR system. Moreover, the study of environmental factors provided valuable insight into the limitations of LPR systems in various environmental and traffic conditions.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/26877813
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Supplemental Notes:
- © 2020 M.C. Rademeyer et al.
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Authors:
- M. C. Rademeyer
- A. Barnard
- M. J. Booysen
- Publication Date: 2020
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 15-28
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Serial:
- IEEE Open Journal of Intelligent Transportation Systems
- Volume: 1
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2687-7813
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Automatic license plate readers; Cameras; Crimes; Crowdsourcing; License plates
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01787343
- Record Type: Publication
- Files: TRIS
- Created Date: Nov 5 2021 11:54AM