Detecting, classifying, and mapping retail storefronts using street-level imagery

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep learning approach that takes storefronts from street-level imagery as input, and directly provides the geo-location and type of commercial function as output. Our method showed a recall of 89.05% and a precision of 88.22% on a real-world dataset of street-level images, which experimentally demonstrated that our approach achieves human-level accuracy while having a remarkable run-time efficiency compared to methods such as Faster Region-Convolutional Neural Networks (Faster R-CNN) and Single Shot Detector (SSD).

Original languageEnglish
Title of host publicationICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery (ACM)
Pages495-501
Number of pages7
ISBN (Electronic)978-1-4503-7087-5
DOIs
Publication statusPublished - 2020
Event10th ACM International Conference on Multimedia Retrieval - Dublin, Ireland
Duration: 26 Jun 202029 Jun 2020
http://www.icmr2020.org/

Conference

Conference10th ACM International Conference on Multimedia Retrieval
Abbreviated titleICMR 2020
CountryIreland
CityDublin
Period26/06/2029/06/20
Internet address

Keywords

  • Convolutional neural networks
  • Object detection
  • Street-level imagery
  • Urban data extraction

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