scholarly journals Automated Vehicle Security System using Convolutional Neural Networks and Support Vector Machine

2019 ◽  
Vol 8 (10) ◽  
pp. 4057-4063
Keyword(s):  
Character Recognition ◽  
Global Economy ◽  
Real Life ◽  
Security System ◽  
Support Vector ◽  
License Plate ◽  
Model Accuracy ◽  
Public Places ◽  
Automated Vehicle ◽  
Vehicle Security

With the rise in the infrastructure in the global economy, there is a need to impact the growth of security systems such as enhancing the security of vehicles at public places, societies or places with crowd. This could be done by keeping up with the monitoring of vehicles through vehicle License Plate Recognition (LPR). Since the numbers of vehicles are increasing on road day by day, it is essential to bring automation in its detection and recognition procedure. The objective of this presented work is to model a real time application to recognize license plate from a vehicle at parking of any society or public places via surveillance cameras. This paper mainly focuses on implementing the concept of component security which is marked by the presence of a blended system with car license plate recognizer as well as face recognizer recognizing its real owner. In proposed Automated Vehicle Security System (AVSS), the achievable model accuracy for Automated LPR model is 94% marked with the use of Tyserract for character recognition and model accuracy for facial recognition is raised to a mark of 83%. This model provides remarkable results and a need of another system where the owner or permitted drivers for a vehicle are mapped to vehicle license plate which could be made to use as collaboration to make it a real life deployable application.

scholarly journals Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code

2018 ◽  
Vol 14 (1) ◽  
pp. 19-25
Author(s):  
Taufik Fuadi Abidin ◽  
Abbas Adam AzZuhri ◽  
Fitri Arnia
Keyword(s):  
Support Vector Machines ◽  
Character Recognition ◽  
Classification Problem ◽  
The Other ◽  
Support Vector ◽  
License Plate ◽  
Chain Code ◽  
Area Code ◽  
Vector Machines ◽  
Multi Class Classification

A license plate is one of the vehicle identities. It consists of alphabetic characters and numbers and represents provincial and area code where the vehicle is registered. This article discusses the character recognition of plate number using zoning and Freeman Chain Code (FCC). Zoning divides character image into several zones i.e. 4, 6, and 8, and then, the pattern of each character in the zone is extracted using FCC as the numerical features. The character is then classified using Support Vector Machines (SVM). It is a multi-class classification problem with 36 categories. The results show that FCC features with 8 zones give the best accuracy (87%) when compared to the other two zones.

A Dynamic Sliding Window Based on Otsu Method for Binary License Plate and Character Recognition

2013 ◽  
Vol 397-400 ◽  
pp. 2301-2308
Author(s):  
Rui Jian ◽  
Jun Zhao
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Grid Method ◽  
Sliding Window ◽  
Recognition Algorithm ◽  
Least Square ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Dynamic Sliding Window

This paper is concerned with the problem of license plate recognition of vehicles. A recognition algorithm based on dynamic sliding window to binarize license plate characters is proposed. While a connected domain approach is presented to cope with the degradation characters. There are three steps to recognize the characters. First, the characters are classified by their features. Then, based on such classification a grid method is used to construct the feature vector. Finally, least square support vector machine is employed to recognize these characters. The test results show the high recognition rate and also illustrate the effectiveness of the proposed algorithm.

scholarly journals Automatic Iranian Vehicle License Plate Recognition System Based on Support Vector Machine (SVM) Algorithms

2013 ◽  
Vol 2 (1) ◽  
pp. 161-174
Author(s):  
Mahdi Aghaie ◽  
Fatemeh Shokri ◽  
Meisam Yadolah Zade Tabari
Keyword(s):  
Character Recognition ◽  
Intelligent System ◽  
Recognition System ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
The Road ◽  
Vehicle License Plate Recognition ◽  
Plate Location ◽  
On The Road

There are far more cars on the road now than there used to be. Therefore, Controlling and managing the huge volume of traffic is virtually impossible without the use of computer technology. This paper represents design and implement of an intelligent system for license plate recognition based on three main steps. This process includes the detection of license plate location, character segmentation and character recognition. In this study, we used Classifier svm to detect the characters. According to the results, the performance of the proposed system is better compared to similar algorithms such as neural network. It is worth mentioning that Recognition Approach is tested in various conditions and results are described.   Keyword- Vehicle license plate recognition, Morphology Operations, Histogram, The edge detection, Classifier SVMDOI: 10.18495/comengapp.21.161174

scholarly journals Optical Character Recognition of Amharic Documents

2007 ◽  
Vol 3 (2) ◽  
Author(s):  
Million Meshesha ◽  
C V Jawahar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Information Needs ◽  
Real Life ◽  
Principal Component ◽  
Support Vector ◽  
Extensive Literature ◽  
Large Set ◽  
Linear Discriminant ◽  
Optical Character

In Africa around 2,500 languages are spoken. Some of these languages have their own indigenous scripts. Accordingly, there is a bulk of printed documents available in libraries, information centers, museums and offices. Digitization of these documents enables to harness already available information technologies to local information needs and developments. This paper presents an Optical Character Recognition (OCR) system for converting digitized documents in local languages. An extensive literature survey reveals that this is the first attempt that report the challenges towards the recognition of indigenous African scripts and a possible solution for Amharic script. Research in the recognition of African indigenous scripts faces major challenges due to (i) the use of large number characters in the writing and (ii) existence of large set of visually similar characters. In this paper, we propose a novel feature extraction scheme using principal component and linear discriminant analysis, followed by a decision directed acyclic graph based support vector machine classifier. Recognition results are presented on real-life degraded documents such as books, magazines and newspapers to demonstrate the performance of the recognizer.

scholarly journals An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

2020 ◽  
Vol 10 (6) ◽  
pp. 2165 ◽  
Author(s):  
Muhammad Ali Raza ◽  
Chun Qi ◽  
Muhammad Rizwan Asif ◽  
Muhammad Armoghan Khan
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Generalized Solution ◽  
Recognition System ◽  
Vital Role ◽  
Transport Systems ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model

License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

scholarly journals Design and Development of Vehicle Theft Detection, Tracking and Accident Identifier System using IoT

2021 ◽  
Vol 23 (07) ◽  
pp. 420-425
Author(s):  
Tejashwini R ◽  
◽  
Dr. Subodh Kumar Panda ◽  
Keyword(s):  
Low Cost ◽  
Security System ◽  
Alarm System ◽  
Security Systems ◽  
Common People ◽  
Public Places ◽  
Vehicle Theft ◽  
Wide Range ◽  
Vehicle Security ◽  
The Internet Of Things

Vehicle security is one of the major concerns that the entire world is currently experiencing. People generally own automobiles, yet these automobiles are not always secure. Vehicle theft occurs in parking lots, public places, and other unsafe areas. The vehicle’s manufacturer does not consider the vehicle security system to be a factor in the overall cost of the vehicle. Nowadays, only a few vehicles come equipped with high-priced security systems. Door locking, alarm system, GSM, GPS, and other security features are built into high-end vehicles only. There is a necessity to build a low-cost security system for vehicles that common people can afford it and the manufacture can built-in the security system in a wide range of automobiles. This paper proposed a method for vehicle theft detection, tracking, and accident identification using the Internet of Things.

scholarly journals Improvised Guard for Next Level Security Automated Vehicle Security System using Artificial Intelligence and Convolutional Neural Network

2020 ◽  
Vol 5 (7) ◽  
pp. 84-88
Author(s):  
Joel M John ◽  
Noel Phillip Issac ◽  
Jerin Thomas ◽  
Subin Alexander ◽  
Syamraj B S
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Security System ◽  
Vehicle Model ◽  
Deep Convolutional Neural Networks ◽  
Automated Vehicle ◽  
The Core ◽  
Core Technology ◽  
User Data ◽  
Vehicle Security

This paper details fully automated vehicle security system involving vehicle model, make detection, driver face recognition and parking system guided by a virtual assistant. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs). This system performs face recognition of the driver and vehicle model, make detection and permit access by opening barrier gate. This allows bigger organizations to control and monitor vehicle traffic as well as gain user data for security purpose. For quantitive analysis, we show that our system outperforms the leading vehicle security system. Proposed paper project website is also available at http://www.astound.ga/igns.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

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