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

arXiv: https://arxiv.org/abs/2207.11372 · HTML: https://mlsanity.com/paper/2207.11372/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector](https://mlsanity.com/paper/1909.01754/) (2019)
- [Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach](https://mlsanity.com/paper/2009.10181/) (2020)
- [A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition](https://mlsanity.com/paper/2010.15904/) (2020)
- [An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings](https://mlsanity.com/paper/2004.03337/) (2020)
- [End-to-End Approach for Recognition of Historical Digit Strings](https://mlsanity.com/paper/2104.13666/) (2021)

## Built on by (most cited first, 4 of 4)

- [Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems](https://mlsanity.com/paper/2309.16495/) (2023)
- [Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers](https://mlsanity.com/paper/2410.14705/) (2024)
- [Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach](https://mlsanity.com/paper/2606.20886/) (2026)
- [An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data](https://mlsanity.com/paper/2609.03258/) (2026)

## Abstract (arXiv)

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.