titleToward Parking Spot Occupancy Recognition: A Self-Supervised Approach
authorsLuan Marko Kujavski, Rayson Laroca, Paulo Lisboa de Almeida
date2026-06-18
venuearXiv (preprint, not peer reviewed)
idarXiv:2606.20886
linksabs pdf hf code
abstractAs urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised approach for parking spot occupancy recognition that requires no labeled samples from the target parking lot. Building upon a self-supervised transfer learning fine-tuning protocol, the proposed training strategy consists of two self-supervised stages: first on unlabeled generic data and then on unlabeled target-specific data, followed by supervised fine-tuning using only generic parking lot labels. We adopt SimCLR with a ResNet-50 encoder and evaluate the method under a leave-one-out cross-environment protocol on three public datasets: PKLot, CNRPark-EXT, and PLds. We also introduce a two-stage deployment strategy in which a Strong General Model is initially deployed, followed by a Specialized Model that incorporates unlabeled images collected during the first N days of deployment in a self-supervised manner. Experimental results show that the Strong General Model alone outperforms supervised and self-supervised baselines, achieving an average accuracy of 97.2%, which further improves to 97.8% with the proposed two-stage strategy. These results demonstrate that self-supervised learning enables a scalable and labelefficient solution for real-world parking occupancy monitoring. Our trained models and source code are publicly available at https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition.

cite

@misc{kujavski2026toward,
  title={Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach},
  author={Luan Marko Kujavski and Rayson Laroca and Paulo Lisboa de Almeida},
  year={2026},
  eprint={2606.20886},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.20886}
}