# A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

arXiv: https://arxiv.org/abs/2203.06463 · HTML: https://mlsanity.com/paper/2203.06463/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 109 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning](https://mlsanity.com/paper/1609.04453/) (2016)
- [Where are the Blobs: Counting by Localization with Point Supervision](https://mlsanity.com/paper/1807.09856/) (2018)
- [Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks](https://mlsanity.com/paper/1606.09367/) (2016)
- [Automated Parking Space Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2106.07228/) (2021)

## Built on by (most cited first, 9 of 9)

- [Vehicle Occurrence-based Parking Space Detection](https://mlsanity.com/paper/2306.09940/) (2023)
- [Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems](https://mlsanity.com/paper/2309.16495/) (2023)
- [Automatic Vision-Based Parking Slot Detection and Occupancy Classification](https://mlsanity.com/paper/2308.08192/) (2023)
- [Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers](https://mlsanity.com/paper/2410.14705/) (2024)
- [Using Deep Neural Networks to Quantify Parking Dwell Time](https://mlsanity.com/paper/2411.00158/) (2024)
- [Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System](https://mlsanity.com/paper/2208.08220/) (2022)
- [Smart Camera Parking System With Auto Parking Spot Detection](https://mlsanity.com/paper/2407.05469/) (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)

Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently present a systematic and comprehensive review of existing works that employ such datasets. The literature review identified relevant gaps that require further research, such as the requirement of dataset-independent approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have noticed that several important factors such as the presence of the same cars across consecutive images, have been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis of the datasets also revealed that certain features that should be present when developing new benchmarks, such as the availability of video sequences and images taken in more diverse conditions, including nighttime and snow, have not been incorporated.