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arXiv · 2207.11372

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

Andre G. Hochuli·Alceu S. Britto Jr.·Paulo R. L. de Almeida·Williams B. S. Alves·Fabio M. C. Cagni
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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.

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Andre G. Hochuli, Alceu S. Britto Jr., Paulo R. L. de Almeida, Williams B. S. Alves, Fabio M. C. Cagni. 2022-07-22. Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems. https://arxiv.org/abs/2207.11372

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