scholarly journals A REVIEW STUDY OF TRAFFIC SIGNAL VIOLATION DETECTION USING ARTIFICIAL INTELLIGENCE

2021 ◽  
pp. 459-464
Author(s):  
Dantene Davis ◽  
Abhishek Singh ◽  
Amarjeeth Singh ◽  
Fahad Ahmad
Keyword(s):  
Character Recognition ◽  
Traffic Management ◽  
Image Acquisition ◽  
Automated System ◽  
Raspberry Pi ◽  
Proposed Model ◽  
Ir Sensors ◽  
Canny Edge ◽  
Review Study ◽  
Traffic Rule

In the new evolving world, traffic rule violations have become a central issue for majority of the developing countries. The numbers of vehicles are increasing rapidly as well as the numbers of traffic rule violations are increasing exponentially. Managing traffic rule violations has always been a tedious and compromising task. Even though the process of traffic management has become automated, it’s a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The principal objective of this project is to control the traffic rule violations accurately and cost effectively. The proposed model includes an automated system which uses IR sensors and camera based on Raspberry PI to capture video. The project presents Automatic Number Plate Recognition (ANPR) techniques and other image manipulation techniques for plate localization and character recognition which makes it faster and easier to identify the number plates. After recognizing the vehicle number from number plate, the SMS based module is used to notify the vehicle owners about their traffic rule violation. An additional SMS is sent to Regional Transport Office (RTO) for tracking the report status. KEYWORDS- Automatic Number Plate Recognition (ANPR), Artificial Neural Network, Image acquisition, CNN, Tesseract OCR, Canny Edge Detection.

scholarly journals Smart Transport System using Automatic Number Plate Recognition

2020 ◽  
Vol 9 (3) ◽  
pp. 2051-2056
Keyword(s):  
Transport System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
License Plate ◽  
Shopping Malls ◽  
Canny Edge Detection ◽  
Bank Account ◽  
Optical Character ◽  
Canny Edge ◽  
Traffic Rule

In this paper, a Smart Transport System using Automatic Number Plate Recognition (ANPR) of a vehicle is proposed. License plate number of a vehicle is recognized to check the details of the vehicle like the make, type of the vehicle, checking for traffic rule violations and to collect the toll in hospitals, highways, tech parks, paid parking slots of shopping malls, and supermarkets in real time. This paper proposes a system implementation consisting of modules to capture the video of the vehicle, segmentation of video into frames, optical character recognition (OCR) to recognize the characters present on the number plate using Canny edge detection. After capturing the image, it is processed and the extracted characters are compared with the results in the database. A sample RTO database with all the required information about the vehicle would be created for the purpose of comparison. The obtained result after the validation of the vehicle number plate with different parameters has to be displayed from the master PC onto the monitor which lets the defaulter know of his/her violations. This is done using IOT. Once the violations have been displayed, the corresponding amount would then be deducted from the defaulter’s bank account.

IoT-Based Dynamic Traffic Management and Control for Smart City in India

2021 ◽  
pp. 127-139
Author(s):  
Deepali Kothari ◽  
Anjana Jain ◽  
Arun Parakh
Keyword(s):  
Traffic Control ◽  
Smart City ◽  
Traffic Management ◽  
Cost Effective ◽  
Raspberry Pi ◽  
Traffic Condition ◽  
Traffic System ◽  
Proposed Model ◽  
Traffic Control System ◽  
And Control

The world is becoming smart as IoT is now an integral part of individuals' routine lives. To control any devices at any place and at anytime from anywhere is now just a matter of access. The goal of this work is to provide simple, efficient, cost-effective, and reliable communication system for traffic management. Keeping in view the aim of smart city, after cleanliness, traffic is the major concern nowadays. A case study is presented through proposed model in this work that will help in improving traffic condition of the city. The available data is analyzed and processed through Raspberry-pi. This data is simultaneously being updated at the web server through cloud. Based on the data available in real time, the system enables controlling traffic system dynamically. This helps in reducing congestion and provides fast going way for heavy vehicular traffic. The system can be clubbed with existing centralized traffic control system in the Indore city to manage traffic conditions in a better way.

scholarly journals Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1633 ◽  
Author(s):  
Beom-Su Kim ◽  
Sangdae Kim ◽  
Kyong Hoon Kim ◽  
Tae-Eung Sung ◽  
Babar Shah ◽  
...  
Keyword(s):  
Real Time ◽  
Routing Protocol ◽  
Traffic Management ◽  
Research Area ◽  
Multimedia Data ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Proposed Model ◽  
Event Information ◽  
Firm Model

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

scholarly journals Indian Currency Denomination Recognition and Fake Currency Identification

2021 ◽  
Vol 2089 (1) ◽  
pp. 012008
Author(s):  
B Padmaja ◽  
P Naga Shyam Bhargav ◽  
H Ganga Sagar ◽  
B Diwakar Nayak ◽  
M Bhushan Rao
Keyword(s):  
Template Matching ◽  
Visually Impaired ◽  
Senior Citizens ◽  
Region Of Interest ◽  
Automated System ◽  
Canny Edge Detection ◽  
Matching Algorithm ◽  
Multi Scale ◽  
Currency Note ◽  
Canny Edge

Abstract Visually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that relies on specific security features to help distinguish a legitimate currency note from an illegitimate one is much needed. These features are extracted using the boundary box region of interest (ROI) and Canny Edge detection in OpenCV implemented in Python, and the multi scale template matching algorithm is applied to match the security features and differentiate fake notes from legitimate notes.

scholarly journals IOT Based Traffic Management System

2021 ◽  
pp. 184-187
Author(s):  
Shashank S ◽  
Kiran P ◽  
Nischay D ◽  
Vinay Kumar M ◽  
B R Vatsala ◽  
...  
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Transportation System ◽  
Raspberry Pi ◽  
Urban Residents ◽  
System Of Cities ◽  
Traffic Management System ◽  
Global Population

In 2014, 54% of the total global population was urban residents. The prediction was a growth of nearly 2% each year until 2020 leading to more pressure on the transportation system of cities. Cities should be making their streets run smarter instead of just making them bigger or building more roads. This leads to the proposed system which will use a Raspberry pi and Camera for tracking the number of vehicles leading to time-based monitoring of the system.

scholarly journals AUTOMATED SYSTEM FOR CONTINUOUS MICROALGAE CULTIVATION IN PHOTOBIOREACTORS

2017 ◽  
Vol 16 (2) ◽  
pp. 03
Author(s):  
D. A. L. D’Agostin ◽  
G. M. Domene ◽  
A. S. Oliveira ◽  
M. J. C. Bonfim ◽  
A. B. Mariano
Keyword(s):  
Continuous Production ◽  
Batch Process ◽  
Automated System ◽  
Control Mode ◽  
Raspberry Pi ◽  
Percentage Error ◽  
Monitoring And Control ◽  
Microalgae Cultivation ◽  
Batch Mode ◽  
Continuous Regime

The objective of this work was to design a automate system for microalgae cultivation on a continuous modes in laboratory scale and allow its remote monitoring and control. For this, a sensor were developed is able to measure biomass concentration. The concentration sensor used the principle of light scattering, that is, by measuring the turbidity of the culture medium by the use of a set of phototransistor and green led. It presented an mean absolute percentage error of 8.46% during the experiment. A pH, temperature and light sensor were also installed. The control of all the sensors was accomplished by means of an microcontroller. For remote control and monitoring of the controller, a database was designed and implemented on a Raspberry Pi connected to the network. The graphics and data collected are available on an HTML page that allows changes in the control mode of the photobioreactor, for example by changing the dilution rates. The controller was able to operate the photobioreactor in batch mode, as well as to maintain the culture operating in continuous regime. The continuous production of microalgae biomass in a continuous regime showed productivity 74.5% higher than the traditional batch process and 28.2% higher than semicontinuous cultivation.

scholarly journals Recognition of Pashto Handwritten Characters Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5884
Author(s):  
Muhammad Sadiq Amin ◽  
Siddiqui Muhammad Yasir ◽  
Hyunsik Ahn
Keyword(s):  
Character Recognition ◽  
Test Accuracy ◽  
Forensic Evidence ◽  
Handwritten Character Recognition ◽  
Data Set ◽  
Research Attention ◽  
Handwritten Character ◽  
Proposed Model ◽  
Strong Candidate ◽  
Processing Techniques

Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

scholarly journals Analytical Modeling for a Video-Based Vehicle Speed Measurement Framework

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 160 ◽  
Author(s):  
Mattias Dahl ◽  
Saleh Javadi
Keyword(s):  
Analytical Model ◽  
Traffic Management ◽  
Sampling Rate ◽  
Video Camera ◽  
Frame Rate ◽  
Vehicle Speed ◽  
Temporal Sampling ◽  
Measurement Framework ◽  
Speed Measurement ◽  
Proposed Model

Traffic analyses, particularly speed measurements, are highly valuable in terms of road safety and traffic management. In this paper, an analytical model is presented to measure the speed of a moving vehicle using an off-the-shelf video camera. The method utilizes the temporal sampling rate of the camera and several intrusion lines in order to estimate the probability density function (PDF) of a vehicle’s speed. The proposed model provides not only an accurate estimate of the speed, but also the possibility of being able to study the performance boundaries with respect to the camera frame rate as well as the placement and number of intrusion lines in advance. This analytical model is verified by comparing its PDF outputs with the results obtained via a simulation of the corresponding movements. In addition, as a proof-of-concept, the proposed model is implemented for a video-based vehicle speed measurement system. The experimental results demonstrate the model’s capability in terms of taking accurate measurements of the speed via a consideration of the temporal sampling rate and lowering the deviation by utilizing more intrusion lines. The analytical model is highly versatile and can be used as the core of various video-based speed measurement systems in transportation and surveillance applications.

Research of License Plate Recognition System Based on Labview

2015 ◽  
Vol 734 ◽  
pp. 646-649
Author(s):  
Zhong Hua Hu ◽  
Chen Tang
Keyword(s):  
Virtual Instrument ◽  
Character Recognition ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
License Plate ◽  
Morphological Operations ◽  
License Plate Recognition ◽  
Plate Location ◽  
Traffic Management System

The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.

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