Object tracking using Particle Swarm Optimization for License Plate Detection - Nur Afrina Nasuha, Mohamed Izeham; Zalili, Musa; Nurul Izzatie Husna, Fauzi; Watada, Junzo (IEEE, 2026)
License plate detection (LPD) is a crucial part of traffic surveillance, vehicle monitoring, and smart transportation. This study introduces a PSO-LPD approach, which uses Particle Swarm Optimization (PSO) for License Plate Detection for object tracking. The goal is to improve detection speed and accuracy, especially in tough conditions like varying light, partial obstructions, and complex backgrounds. This method will stay reliable when detecting fast-moving vehicles. Traditional systems often struggle in these situations due to motion blur or quick changes in the scene. This method is experimented with a collection of three video datasets on our own that comprised of a combined total of 613 annotated frames. The results show that the PSO-LPD approach achieves over 75% accuracy and precision, surpassing traditional detection methods. Our research indicates that this technique is promising for both online and offline applications. It provides an effective solution for improving vehicle surveillance and intelligent traffic management systems.