Book ; Online: Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems
2022
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. ...
| 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. Comment: Work submitted to be published on IEEE IJCNN 2022 / WCCI 2022 (July/22) |
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| Keywords | Computer Science - Computer Vision and Pattern Recognition |
| Subject code | 006 |
| Publishing date | 2022-07-22 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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