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

TL;DR

This paper systematically reviews publicly available datasets for computer vision-based parking lot management, highlighting gaps and suggesting directions for future research to improve robustness and realism.

Contribution

It provides a comprehensive comparison of parking lot image datasets and identifies key gaps in current research and dataset features for future development.

Findings

Existing datasets lack diversity in conditions like nighttime and snow.

Many studies neglect factors like the presence of same cars across images.

Current assessment protocols are often unrealistic due to dataset limitations.

Abstract

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.