Extraction of image parking spaces in intelligent video surveillance systems

Bohush, Rykhard; Yarashevich, Pavel; Ablameyko, Sergey; Kalganova, Tatiana

doi:10.22630/mgv.2018.27.1.3

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Mora et al [14] used a scale-invariant feature transform (SIFT) and an SVM with a radial basis kernel as the classifier to extract features; they tested their model on the PKLot dataset while considering variations in the camera angle, climate, and parking lot configurations. Bohush et al [15] utilized the HOG descriptor as a feature, as well as an SVM classifier; their method was evaluated on a small part of the PKLot dataset, which included 2,135 images, but they did not make clear how images were selected. Vítek and Melničuk [16] introduced a lightweight approach specifically designed for smart cameras that involves combining data concerning the HOG feature vector with the parking angle to improve the results; the proposed method was tested on the PKLot dataset and two private datasets.…”

Section: Feature Extraction-based Methodsmentioning

confidence: 99%

Parking Lot Occupancy Detection with Improved MobileNetV3

Yuldashev,

Mukhiddinov,

Abdusalomov

et al. 2023

Sensors

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In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces. We propose an advanced solution by harnessing an optimized MobileNetV3 model with custom architectural enhancements, trained on the CNRPark-EXT and PKLOT datasets. The model processes individual parking space patches from real-time video feeds, providing occupancy classification for each patch, identifying occupied or available spaces. Our architectural modifications include the integration of a convolutional block attention mechanism in place of the native attention module and the adoption of blueprint separable convolutions instead of the traditional depth-wise separable convolutions. In terms of performance, our proposed model exhibits superior results when benchmarked against state-of-the-art methods. Achieving an exceptional area under the ROC curve (AUC) value of 0.99 for most experiments with the PKLot dataset, our enhanced MobileNetV3 showcases its exceptional discriminatory power in binary classification. Benchmarked against the CarNet and mAlexNet models, representative of previous state-of-the-art solutions, our proposed model showcases exceptional performance. During evaluations using the combined CNRPark-EXT and PKLot datasets, the proposed model attains an impressive average accuracy of 98.01%, while CarNet achieves 97.03%. Beyond achieving high accuracy and precision comparable to previous models, the proposed model exhibits promise for real-time applications. This work contributes to the advancement of parking lot occupancy detection by offering a robust and efficient solution with implications for urban mobility enhancement and resource optimization.

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Section: Feature Extraction-based Methodsmentioning

confidence: 99%

Parking Lot Occupancy Detection with Improved MobileNetV3

Yuldashev,

Mukhiddinov,

Abdusalomov

et al. 2023

Sensors

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show abstract

“…The HOG descriptor's used as features and the SVM classifier is explored by Bohush et al (2018) and by Vítek & Melničuk (2018). In Bohush et al (2018), a subset of the PKLot dataset containing 2,135 images is used for the tests.…”

Section: Bag Of Features Representations Of the Features Is Employed ...mentioning

confidence: 99%

“…The presence of cars may hinder correct detection, especially for methods that rely on the painted demarcations in the parking lots that delimits the parking spaces. An automatic approach for detecting parking spaces using classical image processing methods is proposed in Bohush et al (2018). The approach uses perspective transformation in the entire input image.…”

Section: Automatic Parking Space Detectionmentioning

confidence: 99%

“…The human effort to label training samples from the parking lot is more significant than manually marking each parking space. So how can we expect to use labeled images from the test parking lot to train a method developed for this task, as done in (Li et al, 2017;Bohush et al, 2018;Padmasiri et al, 2020;Kirtibhai Patel & Meduri, 2020;? With the same reasoning, methods should not rely on camera parameters, such as the distance from the lens to the parking lot, since these parameters are difficult to acquire in the real world.…”

Section: Automatic Parking Space Detectionmentioning

confidence: 99%

See 1 more Smart Citation

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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Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System

Duong

Bui

et al. 2023

Lecture Notes in Computer Science

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Extraction of image parking spaces in intelligent video surveillance systems

Cited by 7 publications

References 19 publications

Parking Lot Occupancy Detection with Improved MobileNetV3

Parking Lot Occupancy Detection with Improved MobileNetV3

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

Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System

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