A template-guided approach to vehicle surveillance and access control

Anagnostopoulos, Christos; Alexandropoulos, T.; Boutas, S.; Loumos, V.; Kayafas, E.

doi:10.1109/avss.2005.1577325

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…This can be combined with other techniques to improve results. In [ 49 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ], the scan line technique is utilized where the peaks are formed from the color transition of the grey scale level which corresponds to the number of characters on the number plate. In [ 57 ], the authors proposed the horizontal line scanning with multiple thresholds technique for plate detection in a real time/complex images.…”

Section: Number Plate Extraction Methodsmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

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Section: Number Plate Extraction Methodsmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…The method uses image statistics such as the standard deviation and the mean value as a "heuristic" for possible plate location. In two concentric windows A and B of different sizes (X 1 × Y 1 and X 2 × Y 2 , respectively), which scan the image from left to right and from top to bottom, the mean value [39], [40] or the standard deviation [41] is calculated (see the top part of Fig. 3).…”

Section: ) Hierarchical Representationsmentioning

confidence: 99%

“…The objects that disagree with the alignment are removed. In addition, an adaptive binarization method is described in [39] and [40], implementing the SCW technique using the standard deviation measurement in conjunction with CCA measurements (see Fig. 10).…”

Section: B Gray-level Processing 1) Histogram Processingmentioning

confidence: 99%

“…Moreover, for under-vehicle inspection, it is assumed that a template under-vehicle image for each inspected vehicle has been archived into a database in advance. Based on the incoming vehicle LP, the respective template image is retrieved from the database and then compared to the one acquired during real-time under-vehicle inspection [40]. With a binocular arrangement of the image capturing system, the spatial and temporal range of plate image between consecutive views could provide an improved accuracy in speed computation of the vehicle [145].…”

Section: A Current Trendsmentioning

confidence: 99%

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License Plate Recognition From Still Images and Video Sequences: A Survey

Anagnostopoulos

Psoroulas

et al. 2008

IEEE Trans. Intell. Transport. Syst.

512

248

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License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.

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“…A self-organized Neural Network based on Kohonen's Self-Organized Feature Maps (SOFMs) was implemented to tolerate noisy, deformed, broken, or incomplete characters acquired from licence plates, which were bent or tilted with respect to the camera [9]. Probabilistic Neural Networks (PNNs) for LPR were also introduced by Anagnostopoulos et al [10]. Hausdorff distance is a method for LPCR that compares two binary images.…”

Section: Fig 1 Diagram Of Our Lpr Processmentioning

confidence: 99%

Licence Plate Character Recognition Using Artificial Immune Technique

Huang

Tawfik

Nagar

2008

Computational Science – ICCS 2008

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Abstract. This paper proposes the application of Artificial Immune Technique in Licence Plate Character Recognition (LPCR). The use of Clonal Selection Algorithm (CSA) is composed of two main stages: (1) dynamic training samples; and (2) a choice of the best antibodies based on the three main clonal operations of cloning, clonal mutation and clonal selection. Once memory cells are established it will output the classification results using Fuzzy K-Nearest Neighbor (KNN) approach. The performance of CSA is compared to the Back Propagation Neural Networks (BPNN) in solving a LPCR problem. The experimental results show that the Artificial Immune Technique has a favorable performance in terms of being more accurate and robust.

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A template-guided approach to vehicle surveillance and access control

Cited by 13 publications

References 13 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

License Plate Recognition From Still Images and Video Sequences: A Survey

Licence Plate Character Recognition Using Artificial Immune Technique

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