Image-Based Parking Space Occupancy Classification: Dataset and Baseline
This work addresses the problem of parking space occupancy classification for urban management, but it is incremental as it focuses on dataset creation and a baseline model.
The authors introduced ACPDS, a new dataset for image-based parking space occupancy classification with unique views and systematic annotations, and proposed a baseline model that achieved 98% accuracy on unseen parking lots, outperforming existing models.
We introduce a new dataset for image-based parking space occupancy classification: ACPDS. Unlike in prior datasets, each image is taken from a unique view, systematically annotated, and the parking lots in the train, validation, and test sets are unique. We use this dataset to propose a simple baseline model for parking space occupancy classification, which achieves 98% accuracy on unseen parking lots, significantly outperforming existing models. We share our dataset, code, and trained models under the MIT license.