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).
|Title of host publication||ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||7|
|Publication status||Published - 2020|
|Event||10th ACM International Conference on Multimedia Retrieval - Dublin, Ireland|
Duration: 26 Jun 2020 → 29 Jun 2020
|Conference||10th ACM International Conference on Multimedia Retrieval|
|Abbreviated title||ICMR 2020|
|Period||26/06/20 → 29/06/20|
Bibliographical noteGreen 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.
- Convolutional neural networks
- Object detection
- Street-level imagery
- Urban data extraction