Name
Abstract | ||
|---|---|---|
•We propose an effective CNN architecture for visual parking occupancy detection.•The CNN architecture is small enough to run on smart cameras.•The proposed solution performs and generalizes better than other SotA approaches.•We provide a new training/validation dataset for parking occupancy detection. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.eswa.2016.10.055 | Expert Systems with Applications |
Keywords | Field | DocType |
Machine learning,Classification,Deep learning,Convolutional neural networks,Parking space dataset | Data mining,Architecture,Parking lot,Machine vision,Viewpoints,Computer science,Convolutional neural network,Smart camera,Occupancy,Artificial intelligence,Deep learning,Machine learning | Journal |
Volume | Issue | ISSN |
72 | C | 0957-4174 |
Citations | PageRank | References |
20 | 0.85 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Amato | 1 | 505 | 106.68 |
| Fabio Carrara | 2 | 29 | 8.17 |
| Fabrizio Falchi | 3 | 459 | 55.65 |
| Claudio Gennaro | 4 | 490 | 57.23 |
| Carlo Meghini | 5 | 446 | 77.75 |
| Claudio Vairo | 6 | 97 | 11.35 |