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    Object Geolocation from Crowdsourced Street Level Imagery

    Krylov, Vladimir and Dahyot, Rozenn (2018) Object Geolocation from Crowdsourced Street Level Imagery. ECML PKDD 2018 Workshops, 11329. pp. 79-83.

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    We explore the applicability and limitations of a state-of-the-art object detection and geotagging system [4] applied to crowdsourced image data. Our experiments with imagery from Mapillary crowdsourcing platform demonstrate that with increasing amount of images, the detection accuracy is getting close to that obtained with high-end street level data. Nevertheless, due to excessive camera position noise, the estimated geolocation (position) of the detected object is less accurate on crowdsourced Mapillary imagery than with high-end street level imagery obtained by Google Street View.

    Item Type: Article
    Keywords: Crowdsourced street level imagery; Object geolocation; Traffic light;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15249
    Identification Number:
    Depositing User: Rozenn Dahyot
    Date Deposited: 17 Jan 2022 12:39
    Journal or Publication Title: ECML PKDD 2018 Workshops
    Publisher: Springer
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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