A Vision-Based Method for Parking Space Surveillance and Parking Lot Management

Wang, Ying; Hu, Yangyang; Hu, Xuefeng; Zhao, Yong

doi:10.1007/978-3-319-21978-3_45

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…According to Wang et al [11], the authors proposed an innovative vision-based parking space management system for parking lot management. This system consists of three parts: firstly, a multi-feature (edge and color) based background model using YCrCb color space is used, and a foreground feature extraction method is used to detect the unoccupied parking space.…”

Section: Background Subtraction and Frame Differencing Approachesmentioning

confidence: 99%

Computer Vision-based Approaches, Datasets, and Applications of Smart Parking Systems: A Review

Hadi,

George,

Ahmed

2024

Iraqi Journal of Science

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Nowadays, smart parking systems (SPS) represent one of the active research areas of intelligent transport systems (ITS). It is an interesting topic and attractive to researchers due to the difficult challenges that are needed to solve it. It enhances city life and plays an important role in reducing gas emissions, air pollution, and traffic congestion while improving the time override for drivers to easily allocate vacant parking lots. In this paper, we discuss and dedicate a review of how computer vision-based approaches can assist in providing wellness solutions for the occupancy of smart parking lots. In addition, we provide an overview of the most common standard datasets and some well-known applications of vacant smart parking lots.

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Section: Background Subtraction and Frame Differencing Approachesmentioning

confidence: 99%

Computer Vision-based Approaches, Datasets, and Applications of Smart Parking Systems: A Review

Hadi,

George,

Ahmed

2024

Iraqi Journal of Science

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“…HOGs divide an image into many cells and generate one histogram for each cell; in turn, the histograms consist of the magnitudes and directions of all the pixel gradients in the cells. Several authors have used HOGs to train conventional machine learning algorithms, such as SVM and k-nearest neighbor [15] or Bayesian hierarchical frameworks, which are statistical models [16]. Additionally, there are many feature extraction methods, and these methods require the use of processed pixel colors for template matching [13,17], the application of a morphological operation to binary images [18], or the extraction of texture information through local binary patterns and local phase quantization [19].…”

Section: Introductionmentioning

confidence: 99%

Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection

Hwang,

Cho,

Choi

2023

Applied Sciences

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Vacant parking slot detection using image classification has been studied for a long time. Currently, deep neural networks are widely used in this research field, and experts have concentrated on improving their performance. As a result, most experts are not concerned about the features extracted from the images. Thus, no one knows the crucial features of how neural networks determine whether a particular parking slot is full. This study divides the structures of neural networks into feature extraction and classification parts to address these issues. The output of the feature extraction parts is visualized through normalization and grayscale imaging. The visualized feature maps are analyzed to match the feature characteristics and classification results. The results show that a specific region of feature maps is activated if the parking slot is full. In addition, it is verified that different networks whose classification parts are identical extract similar features from parking slot images. This study demonstrates that feature map analyses help us find hidden characteristics of features and understand how neural networks operate. Our findings show a possibility that handcrafted algorithms using the features found by machine learning algorithms can replace neural network-based classification parts.

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“…This method does not consider whether a vehicle is parked in a parking space. Wang et al [4] used Sobel edge detection to detect a vehicle in a parking space, and a parking space was considered occupied by a vehicle if the percentage of edge pixels exceeded 5% of the total pixels; otherwise, the parking space was considered available. The disadvantage of this method was the poor identification of parking spaces in outdoor parking lots.…”

Section: Introductionmentioning

confidence: 99%

EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions

Wang

Chen

2022

Sensors

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The parking problem, which is caused by a low parking space utilization ratio, has always plagued drivers. In this work, we proposed an intelligent detection method based on deep learning technology. First, we constructed a TensorFlow deep learning platform for detecting vehicles. Second, the optimal time interval for extracting video stream images was determined in accordance with the judgment time for finding a parking space and the length of time taken by a vehicle from arrival to departure. Finally, the parking space order and number were obtained in accordance with the data layering method and the TimSort algorithm, and parking space vacancy was judged via the indirect Monte Carlo method. To improve the detection accuracy between vehicles and parking spaces, the distance between the vehicles in the training dataset was greater than that of the vehicles observed during detection. A case study verified the reliability of the parking space order and number and the judgment of parking space vacancies.

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A Vision-Based Method for Parking Space Surveillance and Parking Lot Management

Cited by 10 publications

References 12 publications

Computer Vision-based Approaches, Datasets, and Applications of Smart Parking Systems: A Review

Computer Vision-based Approaches, Datasets, and Applications of Smart Parking Systems: A Review

Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection

EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions

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