Unmanned Aerial Vehicle–Based Traffic Analysis: Methodological Framework for Automated Multivehicle Trajectory Extraction

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.

Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions

“…Although the authors extracted no vehicle trajectories, their framework performed well in estimating speed but showed slightly lower performance in counting vehicles. Khan et al [8] proposed an automated framework based on background subtraction using optical flow and blob analysis. These authors tested their framework on a four-street intersection but performed no quantitative evaluation of vehicle trajectories.…”

“…Since all images were obtained from a vertical direction above the traffic, this eliminated the need to calibrate the camera. In some cameras, the fish-eye effect must be removed [8], which was not necessary in our case. Also, since UAVs can hover and produce videos that maintain almost constant coordinates among frames, we were able to use the manually extracted road coordinates from the first frames for all the frames.…”

“…Although, we focused on the automated process excluding preprocessing, real-time approaches require automated road extraction and camera calibration. Further details about these processes are presented in other work [8,35].…”

“…To conduct a reliable analysis of trajectory data, a framework is needed to efficiently and accurately extract the data. For more than 40 years, researchers have gathered such valuable data by applying the vision-based detection techniques to track vehicles from aerial platforms [5][6][7][8][9] and surveillance cameras mounted at elevated locations [10][11][12].…”

“…Although some studies have tried to extract vehicle trajectories from UAV images, they have limited success in improving accuracy due to the use of conventional vehicle detection methods, such as background subtraction, optical flow, and blob analysis. These approaches cannot robustly detect the exact location of vehicles in congested traffic, which results in false tracks in the tracking process [7][8][9]18].…”