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Drone-based traffic flow estimation and tracking using computer vision

dc.contributor.authorDe Bruin, A.
dc.contributor.authorBooysen, Marthinus J.
dc.date.accessioned2015-07-03T08:06:28Z
dc.date.available2015-07-03T08:06:28Z
dc.identifier.urihttp://hdl.handle.net/10019.1/97192
dc.description34th Annual Southern African Transport Conference SATC 2015 - Theme - Transport: Working together to deliver – ‘SAKHA SONKE', 6 to 9 July 2015, CSIR International Convention Centre, Pretoria, South Africa .en_ZA
dc.descriptionThe original proceedings are available at http://repository.up.ac.za/handle/2263/5201en_ZA
dc.description.abstractTraffic management has become increasingly important with growth in vehicle numbers unmatched by investment in infrastructure. A large part of management is measuring traffic flow. Video footage of traffic flow is normally manually checked to determine key traffic metrics, consuming many human hours. Moreover, installation and maintenance cost of recording equipment and supporting infrastructure is substantial, especially in the Sub-Saharan context. This paper proposes a novel solution to automate traffic flow estimation, using computer vision. The paper also introduces the notion of making the recording equipment mobile by using drone-based equipment, negating the need for fixed recording installations. The results demonstrate measurement accuracies of 100% down to 81% from ideal to worst case conditions, and successful implementation of drone control algorithms.en_ZA
dc.language.isoen_ZAen_ZA
dc.subjectTraffic managementen_ZA
dc.subjectTraffic flow managementen_ZA
dc.subjectAutomate traffic flow estimationen_ZA
dc.subjectDrone-based equipmenten_ZA
dc.titleDrone-based traffic flow estimation and tracking using computer visionen_ZA
dc.typeConference Paperen_ZA


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