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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23348

Title: Unmanned Aerial Vehicle-Based Traffic Analysis: A Methodological Framework for Automated Multi-Vehicle Trajectory Extraction
Authors: Khan, Muhammad Arsalan
Ectors, Wim
Bellemans, Tom
Janssens, Davy
Wets, Geert
Issue Date: 2017
Citation: 2017 TRB Annual Meeting: Compendium of Papers, p. 25-33
Abstract: The Unmanned Aerial Vehicles (UAVs) commonly referred to as drones, are considered as one of the most dynamic and multi-dimensional emerging technologies of the modern era. Recently, this technology has found multiple potential applications in the transportation field as well, ranging from traffic surveillance applications to traffic network analysis. However, in order to conduct a UAV-based traffic study, an extremely diligent planning and execution is required followed by an optimal data analysis and interpretation procedure. In this paper, however the main focus is on the processing and analysis of the UAV-acquired traffic footage. A detailed methodological framework for the automated UAV video processing is proposed in order to extract the trajectories of multiple vehicles at a particular road segment. Such trajectories can then be used either to extract various traffic parameters or to analyze traffic safety situation. The proposed framework provides a comprehensive guideline for an efficient processing and analysis of a UAV-based traffic study. It is classified into the following five components: (i) pre15 processing, (ii) stabilization, (iii) geo-registration, (iv) vehicle detection and tracking, and (v) trajectory management. Up till now, most of the traffic-focused UAV studies have employed either manual or semi-automatic processing techniques. However, this paper comprises an in18 depth description of the proposed automated framework followed by a field experiment conducted in the city of Sint-Truiden, Belgium. The future research will mainly focus on the extension of the applications of the proposed framework in the context of the UAV-based traffic monitoring and analysis.
URI: http://hdl.handle.net/1942/23348
Category: C2
Type: Proceedings Paper
Appears in Collections: Research publications

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