An efficient pyramid multi-level image descriptor: application to image-based parking lot monitoring

Dornaika, Fadi; Hammoudi, Karim; Phan, T. D. A.

doi:10.1007/s11760-019-01512-6

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Several research has been done to solve the parking space detection problem while keeping three factors in consideration, i.e., mind robustness, deployment effort, and maintenance cost. Previous research has been conducted resulting in different methods i.e., multicamera vehicle detection [11], drone-based and aerial image analysis [12,13], image descriptor-based [14], geometric features-based [15], edge-based [16], planebased [17], convolutional neural network [18,19], sensor network [2] [20]. Some of the previous research was done www.ijacsa.thesai.org based on wireless sensor networks.…”

Section: Background Studymentioning

confidence: 99%

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Texture and Color Descriptor Features-based Vacant Parking Space Detection using K-Nearest Neighbors

Saif,

Mahayuddin

2024

IJACSA

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The importance of the detection of vacant parking spaces is increasing gradually. A system capable of detecting vacant parking spaces in real-time can play an important role in saving valuable time for motorists, decreasing traffic jams, and reducing air pollution. Vision-based parking space detection methods are advantageous in terms of installation and maintenance as existing security cameras in a parking area can be used without the requirement of additional hardware and the detection program can be run on a local or a remote server. One major problem of the vision-based detection method in this context is making the model generalized for detection in various weather conditions. This research proposes a hybrid method to detect vacant parking spaces that use texture and color descriptors. A weighted KNN is used for the classification of parking spaces. The proposed method experimented on PKLot, a large benchmarking dataset that contains images of three parking areas in three weather conditions. The proposed model achieves an accuracy of 99.47% on average while training with 10-fold cross-validation and achieves an accuracy of 99.41% accuracy on average while testing with unseen data. The model shows robustness and better performance in terms of accuracy and processing speed. Several comparisons are also done to show how well it performs with methods found in previous research.

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Section: Background Studymentioning

confidence: 99%

“…Research in [11] proposed a method based on dilated convolution neural networks. They claimed their model to be more robust than other methods proposed by research in [14] and [16]. Research in [8] used the dataset that was created by research in [2] named PKLot and compared the results with other research methods.…”

Section: Background Studymentioning

confidence: 99%

Texture and Color Descriptor Features-based Vacant Parking Space Detection using K-Nearest Neighbors

Saif,

Mahayuddin

2024

IJACSA

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“…In the work of Dornaika et al (2019), SVM and k-NN classifiers are trained with textural features extracted from different scales of the images. The authors used subsets of the PKLot and CNRPark datasets.…”

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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Intelligent parking space management: a binary classification approach for detecting vacant spots

Errousso,

Alaoui,

Benhadou

et al. 2024

Multimed Tools Appl

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An efficient pyramid multi-level image descriptor: application to image-based parking lot monitoring

Cited by 4 publications

References 20 publications

Texture and Color Descriptor Features-based Vacant Parking Space Detection using K-Nearest Neighbors

Texture and Color Descriptor Features-based Vacant Parking Space Detection using K-Nearest Neighbors

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

Intelligent parking space management: a binary classification approach for detecting vacant spots

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