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When using vision-based approaches to classify individual parking spacesbetween occupied and empty, human experts often need to annotate the locationsand label a training set containing images collected in the target parking lotto fine-tune the system. We propose investigating three annotation types(polygons, bounding boxes, and fixed-size squares), providing different datarepresentations of the parking spaces. The rationale is to elucidate the besttrade-off between handcraft annotation precision and model performance. We alsoinvestigate the number of annotated parking spaces necessary to fine-tune apre-trained model in the target parking lot. Experiments using the PKLotdataset show that it is possible to fine-tune a model to the target parking lotwith less than 1,000 labeled samples, using low precision annotations such asfixed-size squares.

Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems