Citation

Kim G, Jung HG, Suhr JK. Sensors (Basel) 2023; 23(15): e6688.

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s23156688

PMID

37571472

Abstract

In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the vehicle BFQ using vehicle-mounted cameras, few studies have been conducted using surveillance cameras. Therefore, this paper conducts a comparative study on various approaches for detecting the vehicle BFQ in surveillance camera environments. Three approaches were selected for comparison, including corner-based, position/size/angle-based, and line-based. For comparison, this paper suggests a way to implement the vehicle BFQ detectors by simply adding extra heads to one of the most widely used real-time object detectors, YOLO. In experiments, it was shown that the vehicle BFQ can be adequately detected by using the suggested implementation, and the three approaches were quantitatively evaluated, compared, and analyzed.

Language: en

Keywords

intelligent transportation system; bottom face quadrilateral; deep neural network; surveillance camera; vehicle position estimation; vehicle-to-infrastructure (V2I)