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DC poleHodnotaJazyk
dc.contributor.authorQureshi, Hasham Shahid
dc.contributor.authorWizcorek, Rebecca
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-10-22T04:59:23Z
dc.date.available2019-10-22T04:59:23Z
dc.date.issued2019
dc.identifier.citationJournal of WSCG. 2018, vol. 26, no. 1, p. 75-81.en
dc.identifier.issn1213-6964 (on-line)
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.urihttp://hdl.handle.net/11025/35589
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjecthluboké učenícs
dc.subjectdetekce obrubníkůcs
dc.subjectasistenční systém pro chodcecs
dc.subjectučení se od začátku do koncecs
dc.subjectLeddarcs
dc.subjectmonokameracs
dc.subjectsloučení dat více senzorůcs
dc.subjectkonvoluční neuronové sítěcs
dc.subjectumělé neuronové sítěcs
dc.titleCurb Detection for a Pedestrian Assistance System using End-to-End Learningen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedOur goal is to develop an assistance system for supporting road crossing among older pedestrians. In order to accomplish this, we propose detecting the curb stone from the pedestrians’ point of view. Curb detection plays a significant role in road detection and obstacle avoidance, etc. However, it also presents significant challenges such as the small size of the target as well as, obstacles and different structures. To tackle these problems, we chose to fuse two sensors, a Camera and a Leddar, and use an algorithm that applies an end-to-end learning approach. The convolutional neural network was chosen to process the images acquired from the mono camera by filming the curb and its surroundings. The artificial neural network was selected to process the point cloud data of the Leddar acquired in the form of arrays from the 16 channels of the Leddar. A prototype was developed for data collection and testing purposes. It consists of a structure carrying both sensors mounted on a walker. The data from both sensors were collected with multiple factors taken into consideration, such as, weather, light conditions and, approaching angles. For the training of algorithms, an end-to-end learning approach was selected where we labelled the complete image or array rather than labelling the individual pixels or points in the data. The networks were trained and, the features from the parallel networks were concatenated and given as the input to the fully connected layers to train the complete network. The experimental results show an accuracy of more than 99% and robustness of the end-to-end learning approach. Both sensors are relatively inexpensive and are in fusion together, they are able to efficiently accomplish the task of detecting the curb stone from the pedestrians’ point of view.en
dc.subject.translateddeep learningen
dc.subject.translatedcurb detectionen
dc.subject.translatedpedestrian assistance systemen
dc.subject.translatedend-to-end learningen
dc.subject.translatedLeddaren
dc.subject.translatedmonocameraen
dc.subject.translatedmulti sensor data fusionen
dc.subject.translatedconvolutional neural networksen
dc.subject.translatedartificial neural networksen
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2019.27.1.9
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2019.27.1.9
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 27, Number 1 (2019)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 27 (2019)
  6. Volume 27, Number 1 (2019)