Name
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).
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3372278.3390706 | ICMR '20: International Conference on Multimedia Retrieval
Dublin
Ireland
June, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7087-5 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shahin Sharifi Noorian | 1 | 1 | 0.35 |
| Sihang Qiu | 2 | 2 | 2.38 |
| Achilleas Psyllidis | 3 | 9 | 2.30 |
| Alessandro Bozzon | 4 | 641 | 71.27 |
| Geert-jan Houben | 5 | 2547 | 209.67 |