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Drone-based traffic flow estimation

Stellenbosch University final year project: 2014

Developed by: Andrew de Bruin - 16458826

Abstract:

Drone-based traffic flow estimation and tracking using computer vision

De Bruin, A.; Booysen, M.J.

Traffic flow estimation is a technique by which traffic engineers analyse the extent to which a particular road segment can accommodate traffic. Decisions regarding the need for road upgrades, the installation of speeding cameras and even general security upgrades are made based on these results. Since traffic cameras are usually installed throughout urban areas, exploiting this established infrastructure would allow for the seamless integration of an autonomous traffic flow estimation system. This can be achieved by designing a highly flexible and adaptive system, that would allow for the analyses of traffic footage from various input sources.

The purpose of this project was to design and implement a system that allowed for autonomous traffic flow estimation using computer vision techniques. The system was required to compute traffic flow metrics and upload the information in real time to an online dashboard.

The Mixture of Gaussian (MoG) background subtraction technique was implemented throughout the project as the primary method of vehicle detection. Shadows were detected based on certain chromaticity characteristics, before being subsequently removed from the foreground mask. Relative vehicle velocities were computed based optical flow tracking techniques. Results showed both the vehicle detection as well as the velocity computations to be around 95% accurate under ideal conditions, and around 80% under non-ideal illumination conditions.

A particularly attractive alternative to the static pole mounted traffic cameras, is to use a fully autonomous aircraft. In this way, simple landing platforms could eventually replace expensive traffic cameras. This would allow for the analysis of traffic flow in more rural areas where infrastructure is often non existent. The system designed to automate the aircraft flight control was implemented in the discrete time domain using augmented difference equations. The method of least squares was used to obtain a model of the plant so that appropriate controllers could be designed. The Ziegler-Nichols tuning method was ultimately used to obtain the practical controller parameters. Results obtained during the testing procedure showed that the aircraft was able to land on the designated platform. The inclusion of the automated aircraft is intended to add additional functionality to the primary aim of this project.

The system was designed in a modular fashion, with each submodule contributing additional functionality. The modules were then integrated and thoroughly tested to ensure optimal system performance. Results of the testing showed the system to be fully functional, and proved the concept of autonomous traffic flow estimation to be a viable option.

Music by: "Mark Petrie-Epic Legend"

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