REAL

Capturing the true bounding boxes : vehicle kinematic data extraction using unmanned aerial vehicles

Mi, Tian and Takács, Dénes and Liu, Henry and Orosz, Gábor (2024) Capturing the true bounding boxes : vehicle kinematic data extraction using unmanned aerial vehicles. JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS. ISSN 1547-2450 (In Press)

[img]
Preview
Text
final_Image_process_paper_0303.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (10MB) | Preview

Abstract

This paper presents a methodology by which kinematic variables of road vehicles can be extracted from unmanned aerial vehicle (UAV) footage. The oriented bounding boxes of the vehicles are identified based on the aerial view of the intersection, and the kinematic variables, such as position, longitudinal velocity, lateral velocity, yaw angle and yaw rate, are determined. The bounding boxes are converted to the perspective of a roadside camera using homography, to generate labeled data sets for training the machine learning-based perception systems of smart intersections. Compared to ordinary GPS data-based technology, the proposed method provides smoother data and more information about the dynamics of the vehicles. In the meantime, it does not require any additional instrumentation on the vehicles. The extracted kinematic variables can be used for motion prediction of road traffic participants and for control of connected automated vehicles (CAVs) in intelligent transportation systems.

Item Type: Article
Uncontrolled Keywords: vehicle tracking; unmanned aerial vehicles; video processing; kinematic variables; data sets for machine learning algorithms
Subjects: T Technology / alkalmazott, műszaki tudományok > TL Motor vehicles. Aeronautics. Astronautics / járműtechnika, repülés, űrhajózás
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 18 Sep 2024 10:40
Last Modified: 18 Apr 2025 23:15
URI: https://real.mtak.hu/id/eprint/205131

Actions (login required)

Edit Item Edit Item