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 language | English |
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Title of host publication | ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval |
Publisher | ACM |
Pages | 495-501 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-4503-7087-5 |
DOIs | |
Publication status | Published - 2020 |
Event | 10th ACM International Conference on Multimedia Retrieval - Dublin, Ireland Duration: 26 Jun 2020 → 29 Jun 2020 http://www.icmr2020.org/ |
Conference
Conference | 10th ACM International Conference on Multimedia Retrieval |
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Abbreviated title | ICMR 2020 |
Country/Territory | Ireland |
City | Dublin |
Period | 26/06/20 → 29/06/20 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Keywords
- Convolutional neural networks
- Object detection
- Street-level imagery
- Urban data extraction