Abstract
Abstract. 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.
Highlights
1.1 BackgroundTraffic monitoring systems within urban infrastructure have become an integral component for managing congestion, transport analysis, and for keeping roads safe and efficient
The YoloV2 and Simple Online Realtime Tracking (SORT) algorithms successfully monitored vehicles as they passed through the image space
There were several frames in which the detection algorithm missed a vehicle. This did not impede on the ability of SORT to re-identify the vehicle for continuous speed estimation
Summary
1.1 BackgroundTraffic monitoring systems within urban infrastructure have become an integral component for managing congestion, transport analysis, and for keeping roads safe and efficient. By utilising advanced technology (e.g. deep learning algorithms) in combination with dense and widespread roadside CCTV networks throughout the UK, the greater aim of this paper is to be able to automatically analyse traffic conditions in real-world contexts. In this case, a key component defining traffic conditions – vehicle speeds – will be the focus. Counting the number of passing vehicles will be considered Such information is necessary for understanding fine vehicle movements and subsequent interactions which contribute to road conditions. Vehicle speed information can be used to aid the monitoring of traffic in specific locations – increasing safety measures through emergency response and managing the efficiency and environmental effects of roads by revealing areas of stress
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