Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation
arXiv:2606.04684v1 Announce Type: cross Abstract: The real-time hardships of video processing seriously limit the usage of Automatic License Plate Recognition (ALPR) with application in dynamic traffic monitoring settings. High-fidelity recognition of unconstrained variables, e.g. drastic variations in illumination, acute camera scans, high vehicle speeds, and harsh physical concealment, is a problem that often leads to disjointed tracking paths and poor Optical Character Recognition (OCR) rates. In order to mitigate these weaknesses, the study proposes a 5 stage, end-to-end algorithmic pipeli
The continuous advancements in real-time AI processing and computer vision are pushing the boundaries of what is possible in dynamic monitoring applications.
This development addresses critical limitations in real-time video processing for ALPR, enabling more reliable and effective deployment in demanding environments like dynamic traffic monitoring.
The ability to accurately recognize license plates under challenging conditions with an improved algorithmic pipeline will enhance surveillance, traffic management, and law enforcement capabilities.
- · Traffic monitoring companies
- · Smart city infrastructure developers
- · Law enforcement agencies
- · Computer vision hardware manufacturers
- · Criminals relying on anonymity
- · Legacy ALPR system providers relying on less robust methods
Improved ALPR accuracy leads to more efficient traffic flow management and rapid vehicle identification.
Enhanced surveillance capabilities could contribute to broader public safety initiatives and deter specific types of crime.
The widespread adoption of such robust ALPR systems may raise new questions about privacy and data collection in public spaces.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI