Citation

Shao C, Cheng F, Mao S, Hu J. Big Data 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Mary Ann Liebert Publishers)

DOI

10.1089/big.2021.0311

PMID

35510929

Abstract

Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big multimodal data analysis is proposed. First, the road vehicle data are collected by PCR, where the vehicle length, chassis outline, and height features are extracted as the sample data. Then, the ELM is utilized to learn these three modal features. According to the input feature data, the vehicle type is classified, including cars, sport-utility vehicles, and buses. Finally, the SSA is applied to optimize the initial weights and thresholds of ELM. Experimental results show that SSA-ELM has notable advantages in classification accuracy and convergence speed, compared with existing benchmark methods.

Language: en

Keywords

multimodal data analysis; sparrow search optimization and extreme learning machine; vehicle classification