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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23347

Title: Unmanned Aerial Vehicle–Based Traffic Analysis: Methodological Framework for Automated Multivehicle Trajectory Extraction
Authors: Khan, Muhammad Arsalan
Ectors, Wim
Bellemans, Tom
Janssens, Davy
Wets, Geert
Issue Date: 2017
Abstract: Unmanned aerial vehicles (UAVs), commonly referred to as drones, are one of the most dynamic and multidimensional emerging technologies of the modern era. This technology has recently found multiple potential applications within the transportation field, ranging from traffic surveillance applications to traffic network analysis. To conduct a UAV-based traffic study, extremely diligent planning and execution are required followed by an optimal data analysis and interpretation procedure. In this study, however, the main focus was on the processing and analysis of UAV-acquired traffic footage. A detailed methodological framework for automated UAV video processing is proposed to extract the trajectories of multiple vehicles at a particular road segment. Such trajectories can be used either to extract various traffic parameters or to analyze traffic safety situations. The proposed framework, which provides comprehensive guidelines for an efficient processing and analysis of a UAV-based traffic study, comprises five components: preprocessing, stabilization, georegistration, vehicle detection and tracking, and trajectory management. Until recently, most traffic-focused UAV studies have employed either manual or semiautomatic processing techniques. In contrast, this paper presents an in-depth description of the proposed automated framework followed by a description of a field experiment conducted in the city of Sint-Truiden, Belgium. Future research will mainly focus on the extension of the applications of the proposed framework in the context of UAV-based traffic monitoring and analysis.
Notes: Khan, MA (reprint author), Hasselt Univ, Transportat Res Inst IMOB, Agoralaan, B-3590 Diepenbeek, Belgium. muhammadarsalan.khan@uhasselt.be
URI: http://hdl.handle.net/1942/23347
DOI: 10.3141/2626-04
ISI #: 000413517800005
ISSN: 0361-1981
Category: A1
Type: Journal Contribution
Validation: ecoom, 2018
Appears in Collections: Research publications

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