Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorThirlwell, Andrew
dc.contributor.authorArandjelović, Ognjen
dc.date.accessioned2020-06-03T14:30:04Z
dc.date.available2020-06-03T14:30:04Z
dc.date.issued2020-05-28
dc.identifier.citationThirlwell , A & Arandjelović , O 2020 , ' Big data driven detection of trees in suburban scenes using visual spectrum eye level photography ' , Sensors , vol. 20 , no. 11 , 3051 . https://doi.org/10.3390/s20113051en
dc.identifier.issn1424-8220
dc.identifier.otherPURE: 268315882
dc.identifier.otherPURE UUID: c9b2eed3-7d1e-4539-880e-2054bfc7ee26
dc.identifier.otherBibtex: s20113051
dc.identifier.otherWOS: 000552737900044
dc.identifier.otherScopus: 85085678717
dc.identifier.urihttp://hdl.handle.net/10023/20044
dc.description.abstractThe aim of the work described in this paper is to detect trees in eye level view images. Unlike previous work that universally considers highly constrained environments, such as natural parks and wooded areas, or simple scenes with little clutter and clear tree separation, our focus is on much more challenging suburban scenes, which are rich in clutter and highly variable in type and appearance (houses, falls, shrubs, cars, bicycles, pedestrians, hydrants, lamp posts, etc.). Thus, we motivate and introduce three different approaches: (i) a conventional computer vision based approach, employing manually engineered steps and making use of explicit human knowledge of the application domain, (ii) a more machine learning oriented approach, which learns from densely extracted local features in the form of scale invariant features (SIFT), and (iii) a machine learning based approach, which employs both colour and appearance models as a means of making the most of available discriminative information. We also make a significant contribution in regards to the collection of training and evaluation data. In contrast to the existing work, which relies on manual data collection (thus risking unintended bias) or corpora constrained in variability and limited in size (thus not allowing for reliable generalisation inferences to be made), we show how large amounts of representative data can be collected automatically using freely available tools, such as Google’s Street View, and equally automatically processed to produce a large corpus of minimally biased imagery. Using a large data set collected in the manner and comprising tens of thousands of images, we confirm our theoretical arguments that motivated our machine learning based and colour-aware histograms of oriented gradients based method, which achieved a recall of 95% and precision of 97%.
dc.format.extent15
dc.language.isoeng
dc.relation.ispartofSensorsen
dc.rightsCopyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.subjectComputer visionen
dc.subjectLocal featuresen
dc.subjectMachine learningen
dc.subjectStreet viewen
dc.subjectTree stumpsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.subjectDASen
dc.subject.lccQA75en
dc.subject.lccTen
dc.titleBig data driven detection of trees in suburban scenes using visual spectrum eye level photographyen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.3390/s20113051
dc.description.statusPeer revieweden


This item appears in the following Collection(s)

Show simple item record