Development of portable automatic number plate recognition (ANPR) system on Raspberry Pi

G, S. Fakhar A.; H, M. Saad; K, A. Fauzan; H., R. Affendi; A., Mario A. Gutiérrez

doi:10.11591/ijece.v9i3.pp1805-1813

Cited by 7 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the selection of determining the accuracy of the image, the following shows one example that can be shown one example of the image of Adirondack in Figure 2(a) and Figure 2(b), this can be compared between the proposed algorithm with the algorithm proposed by Fakhar et al [21]. Clearly shows with the proposed algorithm is to generate a more accurate result with less noise.…”

Section: Results and Analysismentioning

confidence: 97%

Stereo matching algorithm using census transform and segment tree for depth estimation

Azali

Hamzah²,

Noh³

et al. 2023

TELKOMNIKA

View full text Add to dashboard Cite

This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively. The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available methods from the benchmark system.

show abstract

Section: Results and Analysismentioning

confidence: 97%

Stereo matching algorithm using census transform and segment tree for depth estimation

Azali

Hamzah²,

Noh³

et al. 2023

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…This stage consists of two main processes which are the post-processing and final disparity map filtering. Post processing, implemented by [21] and [22], normally involves left right (LR) consistency checking and pixels filling-in process. LR checking is applied to detect outliner or the invalid pixels.…”

Section: Disparity Refinementmentioning

confidence: 99%

A study of stereo matching algorithm on low texture and depth discontinuity regions

Gan Yeou Wei,

Affendi Hamzah,

Syahrim Nik Anwar

et al. 2023

IJEECS

View full text Add to dashboard Cite

<p>This article studies the performance of the proposed stereo matchingalgorithm on complex regions. These regions are areas with very limitedinformation for the matching process which are low texture, and depthdiscontinuity regions. In this study, each algorithm uses different matchingcost computation (MCC) techniques, but for cost aggregation (CA), disparityoptimization (DO) and disparity refinement (DR), the technique remains thesame. The MCC areabsolute difference(AD), the combination ofabsolutedifference and gradient matching(AD+GM) andcensus transform(CT).Then, for CA, DO and DR, they areminimum spanning tree(MST),winnertake all(WTA) andbilateral filter(BF), respectively. The results are presentedand discussed in this article. Hence, thru this study the robust method can beestimated at the MCC stage.</p>

show abstract

“…With the advent of digital camera technology and improved computational processing within handphones and microcomputers, ANPRs are more portable and handheld in recent years [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Live-Video Automatic Number Plate Recognition (ANPR) System Using Convolutional Neural Network (CNN) with Data Labelling on an Android Smartphone

F.¹,

F²,

Hamzah³

et al. 2021

IJETAE

View full text Add to dashboard Cite

Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.

show abstract

Development of portable automatic number plate recognition (ANPR) system on Raspberry Pi

Cited by 7 publications

References 11 publications

Stereo matching algorithm using census transform and segment tree for depth estimation

Stereo matching algorithm using census transform and segment tree for depth estimation

A study of stereo matching algorithm on low texture and depth discontinuity regions

A Live-Video Automatic Number Plate Recognition (ANPR) System Using Convolutional Neural Network (CNN) with Data Labelling on an Android Smartphone

Contact Info

Product

Resources

About