Name
Title | ||
|---|---|---|
Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems |
Abstract | ||
|---|---|---|
When using vision-based approaches to classify individual parking spaces between occupied and empty, human experts often need to annotate the locations and label a training set containing images collected in the target parking lot to fine-tune the system. We propose investigating three annotation types (polygons, bounding boxes, and fixed-size squares), providing different data representations of the parking spaces. The rationale is to elucidate the best trade-off between handcraft annotation precision and model performance. We also investigate the number of annotated parking spaces necessary to fine-tune a pre-trained model in the target parking lot. Experiments using the PKLot dataset show that it is possible to fine-tune a model to the target parking lot with less than 1,000 labeled samples, using low precision annotations such as fixed-size squares. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/IJCNN55064.2022.9892783 | IEEE International Joint Conference on Neural Network (IJCNN) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andre G. Hochuli | 1 | 0 | 0.34 |
| Alceu S. Britto Jr. | 2 | 32 | 3.32 |
| Paulo R. L. de Almeida | 3 | 0 | 0.34 |
| Williams B. S. Alves | 4 | 0 | 0.34 |
| Fabio M. C. Cagni | 5 | 0 | 0.34 |