A Vehicle Matching Algorithm by Maximizing Travel Time Probability Based on Automatic License Plate Recognition Data

Vehicle re-identification aims to match and identify the same vehicle crossing multiple surveillance cameras and obtain traffic information such as travel time. The Automatic License Plate Recognition (ALPR) data are widely employed in urban surveillance. However, vehicle re-identification based on ALPR data is challenging due to license plate recognition errors and unrecognized issues. This paper proposes a vehicle matching algorithm designed to maximize the travel time probability using ALPR data, while accounting for recognition errors and unrecognized issues. The proposed algorithm consists of several modules, including the estimation of travel time distribution, computation of travel time probability, calculation of travel time confidence intervals and matching time window size, restricted fuzzy matching, and vehicle matching optimization. To evaluate the effectiveness of the proposed algorithm across varying lighting and weather conditions, ALPR data was collected from a survey road in four scenarios: sunny day, sunny night, rainy day, and rainy night. The results indicate that when compared to a sunny day scenario, severe lighting and adverse weather conditions lead to decreased matching accuracy and increased matching accuracy errors for all methods evaluated. However, our proposed model outperforms benchmark algorithms in both scenarios, demonstrating its superior performance.