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Lookup NU author(s): Daniel Bell,
Dr Wen Xiao,
Professor Philip James
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 Copernicus GmbH. All rights reserved.A workflow is devised in this paper by which vehicle speeds are estimated semi-automatically via fixed DSLR camera. Deep learning algorithm YOLOv2 was used for vehicle detection, while Simple Online Realtime Tracking (SORT) algorithm enabled for tracking of vehicles. Perspective projection and scale factor were dealt with by remotely mapping corresponding image and real-world coordinates through a homography. The ensuing transformation of camera footage to British National Grid Coordinate System, allowed for the derivation of real-world distances on the planar road surface, and subsequent simultaneous vehicle speed estimations. As monitoring took place in a heavily urbanised environment, where vehicles frequently change speed, estimations were determined consecutively between frames. Speed estimations were validated against a reference dataset containing precise trajectories from a GNSS and IMU equipped vehicle platform. Estimations achieved an average root mean square error and mean absolute percentage error of 0.625 m/s and 20.922 % respectively. The robustness of the method was tested in a real-world context and environmental conditions.
Author(s): Bell D, Xiao W, James P
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: XXIV ISPRS Congress
Year of Conference: 2020
Online publication date: 03/08/2020
Acceptance date: 02/04/2016
Date deposited: 07/01/2021
Publisher: Copernicus GmbH
Series Title: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences