Parking Slot Detection Using GLCM and Similarity Measure

Irfan, A.; Nur, Nahya; Wajidi, Farid

doi:10.1088/1757-899x/875/1/012094

Cited by 1 publication

(1 citation statement)

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“…In addition, a custom-built dataset, including images from the CNRPark and ImageNet (Deng et al, 2009) was used in the tests. Irfan et al (2020) proposed Gray-Level Co-Occurrence Matrixes (GLCM) as texture features. The test images are classified as occupied or empty according to their similarity to the train images.…”

Section: Used Uniformmentioning

confidence: 99%

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

de Almeida,

Alves,

Parpinelli

et al. 2022

Preprint

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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.

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Section: Used Uniformmentioning

confidence: 99%