Parking entrance control using license plate detection and recognition

Self Cite

Due to the increase in number of cars and slow city developments, there is a need for smart parking system. One of the main issues in smart parking systems is parking lot occupancy status classification, so this paper introduce two methods for parking lot classification. The first method uses the mean, after converting the colored image to grayscale, then to black/white. If the mean is greater than a given threshold it is classified as occupied, otherwise it is empty. This method gave 90% correct classification rate on cnrall database. It overcome the alexnet deep learning method trained and tested on the same database (the mean method has no training time). The second method, which depends on deep learning is a deep learning neural network consists of 11 layers, trained and tested on the same database. It gave 93% correct classification rate, when trained on cnrall and tested on the same database. As shown, this method overcome the alexnet deep learning and the mean methods on the same database. On the Pklot database the alexnet and our deep learning network have a close resutls, overcome the mean method (greater than 95%).

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Deep learning versus traditional methods for parking lots occupancy classification

Farag

Din

Elshenbary

2020

Self Cite

“…Split and merge segmentation technique aims to partition a digital image into multiple region to restructure image representation into a meaningful information which is easy to analyse. The image area is divided into smaller uniform areas and these smaller areas are again combined to indicate the area of interest [21][22][23]. There are 4 steps involve in split and merge algorithm which are (a) Divide any area Ri into four equal areas, (b) Divide the area continously until there is no area left, (c) combine any two areas Ri and Rj for which P(Ri ∪ Rj) = TRUE.…”

Section: Split and Merge Segmentationmentioning

confidence: 99%

Automated detection of vehicles license plate using image processing techniques

Po'ad

Othman

Atan

et al. 2020

The aim of this project is to design an Automated Detection of License Plate (ADLP) system based on image processing techniques. There are two techniques that are commonly used in detecting the target, which are the Optical Character Recognition (OCR) and the split and merge segmentation. Basically, the OCR technique performs the operation using individual character of the license plate with alphanumeri characteristic. While, the split and merge segmentation technique split the image of captured plate into a region of interest. These two techniques are utilized and implemented using MATLAB software and the performance of detection is tested on the image and a comparison is done between both techniques. The results show that both techniques can perform well for license plate with some error.

“…Although chromatic-based feature had some improvement in detection rate, the results may still not satisfactory due to minimal feature derived from the skin color. Several researchers utilized color-based such YCoCg-R in watermarking technique [15], HSV in shadow detection [16], cloth retrieval [17] and license plate detection [18].…”

Section: Introductionmentioning

confidence: 99%

A multi-color based features from facial images for automatic ethnicity identification model

Osman

Maarof

Rohani

et al. 2020

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.