Abstract
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.
Highlights
E VERY 10 minutes a motor vehicle is stolen in Brazil while in South Africa an average of 44 vehicles are hijacked every day [1], [2]
To what extent, (1) optoelectronic and (2) environmental factors affect the overall ability of vehicle-mounted license plate recognition (LPR) systems to correctly identify license plates
Additional funds should rather be used to invest in a high-resolution image sensor, enabling the recognition of license plates at a much greater distance, introducing a greater susceptibility to slant distortion during lateral motion
Summary
E VERY 10 minutes a motor vehicle is stolen in Brazil while in South Africa an average of 44 vehicles are hijacked every day [1], [2]. Developing countries are prevented from harnessing the benefits of large scale LPR deployment due to commercially available systems generally being expensive, heavily reliant on proprietary technology and only offered as part of extensive comprehensive system solutions. In such countries, a scalable low-cost open-source LPR system would enable the crowdsourcing of traffic monitoring and vehicle tracking to civilian vehicles, which could leverage their mobility to double up as ubiquitous ‘eyes on the road’. Aggregating this information could greatly contribute to community safety and effective law enforcement in developing countries
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