scholarly journals A Novel Approach to Vehicle Number Identification using Raspberry pi 3

2019 ◽  
pp. 64-67
Author(s):  
N. Varshini ◽  
Sumedha Kasarla ◽  
Shaik Subhani
Keyword(s):  
Real Time ◽  
Traffic Signals ◽  
Raspberry Pi ◽  
Identification System ◽  
License Plate ◽  
Text Format ◽  
Novel Approach ◽  
Image Conversion ◽  
Image Capturing ◽  
Conversion Technology

Vehicle Number Identification using Raspberry pi 3 is an image conversion technology which captures the license plate of a vehicle. The main aim is to make an effective and accurate license number plate identification system. This system is carried out and performed in the areas where traffic signals are present and the camera is placed on the signal which is connected to raspberry pi and it sends signals to the server and it can also be used in apartments or residencies for capturing all the vehicle numbers entering the building. This system at first detects the vehicle license plate and then captures it .It then converts the image into the text. The text of the license plate is displayed on the screen using the image conversion. Open CV and OCR are the two software's used for image capturing and conversion of that into text format respectively. The resulting data is then displayed on the screen and saved into a folder. The whole system is developed on Raspberry Pi desktop and its performance is used in real-time. It is observed from this experiment that the system mainly detects and captures the vehicle license plate, converts the image into text and displays it on the screen successfully.

scholarly journals A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 55
Author(s):  
Nicole do Vale Dalarmelina ◽  
Marcio Andrey Teixeira ◽  
Rodolfo I. Meneguette
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Transportation Systems ◽  
Identification System ◽  
License Plate ◽  
Optical Character ◽  
Smart Parking

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

scholarly journals Implementation paper of Traffic Signal Detection and Recognition using deep learning

2021 ◽  
Vol 12 (1S) ◽  
pp. 212-219
Author(s):  
Nikhil S. Rajguru, Et. al.
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Autonomous Driving ◽  
Traffic Signals ◽  
Raspberry Pi ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Efficient Manner ◽  
Detection And Recognition

Traffic boards and traffic signals are used to maintain proper traffic through busy roads. They help to recognize the rules to follow when driving the vehicle. These signs warn the distracted driver, and prevent his/her actions which could lead to an accident. We have proposed a system which can help recognize these boards and signals at real time thus avoiding major mishap. A real-time automatic sign detection and recognition can help the driver, significantly increasing his/her safety. Lately traffic sign recognition has got an immense interest lately by large scale companies such as Google, Apple and Volkswagen etc. which is driven by the market needs for intelligent applications such as autonomous driving, driver assistance systems (ADAS), mobile mapping, Mobil eye, Apple, etc.  Hence, here, we have implemented to do the same with cost efficient manner using Raspberry Pi. The proposed system detects the traffic board or traffic signals, capture its image which through deep learning approach recognizes the same to give result on dashboard as well it gives the measures of distance from front obstacle which helps to implement brake system if obstacle is near. PiCam is used to capture images of traffic sings and is connected to RaspberryPi. Monitor is used to display required output, showing type of sign and distance of collision. This proposal will avoid large number of accidents occurring at bridges and work in progress area due to automated braking system and simultaneous reduce death ratio.

scholarly journals A Novel Approach to Increase the Efficiency of a Multi-lingual Real-time Speaker Identification System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real Time ◽  
Speech Enhancement ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Cost Effective ◽  
Recognition System ◽  
Identification System ◽  
Novel Approach ◽  
Activity Method ◽  
Voice Activity

Nowadays, the real-time speaker recognition system is very popular due to its cost-effective nature. However, it is a very challenging one to produce a more efficient speaker identification system. In our work, we work on a multi-lingual real-time speaker identification system. We work in a novel way to enhance the efficiency of the said system. We take some real speech signals and use different speech enhancement methods and our proposed voice activity method (VAD) to enhance the efficiency of said system. By doing so, we increase the accuracy of the said system relatively by 2% as compared to existing methods.

scholarly journals Real Time Traffic Signs and Obstacle Detection in Self-Driving Car

2020 ◽  
Vol 9 (6) ◽  
pp. 253-256
Keyword(s):  
Real Time ◽  
Research Work ◽  
Traffic Signals ◽  
Obstacle Detection ◽  
Raspberry Pi ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Detection Model ◽  
Emergency Braking ◽  
Real Time Traffic

The motivation behind this research work is to improve car safety and efficiency.The concept of self driving cars is heard from years, it has not come into usage in many countries because of the lack of complete intelligence in the vehicle. Some of the modern vehicles provide partially automated specifications such as keeping the car within its lane, speed controls or emergency braking..According to statistics most of the accidents occur due to lack of instant response to traffic signs and obstacles ahead. In case of self driving car this problem can be addressed by detecting the traffic signals using high end camera. Real time traffic sign detection model accomplishes its objective by identifying the traffic signals and obstacles. A high end camera is used to capture the image, raspberry pi 3 is used as hardware and open computer vision library is used to process the image and identify the patterns in the image to properly detect the signals. Ultra sonic distance sensor is used to identify the obstacles.

scholarly journals Novel and Real-Time GPU Accelerated Speed Violation Detection and License Plate Identification System

2021 ◽  
Author(s):  
Udaya Dampage ◽  
KKN Hasantha ◽  
HADS Gimhana ◽  
HMTYN Bandara ◽  
SV Maddumage
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Specific Area ◽  
Superior Performance ◽  
Traffic Density ◽  
Maximum Speed ◽  
Identification System ◽  
License Plate ◽  
Starting Point ◽  
Violation Detection

Abstract Commuters lose a lot of time and effort due to the inefficiency of traffic management. Although nowadays most of the processes are automated, it seems a speed violation detection is the least focused area apart from using speed guns which the RADAR may make mistakes and yet, doing such will benefit people by saving their time and let them escape from the troublesome situations. To address this issue, a real-time solution by fully automating the process of detecting the speed violation and the license plates of the offenders is proposed in this paper. A vehicle approaching a specific area will be automatically identified and tracked from a reference starting point. Within the covered range of the camera according to the traffic density present at that instance, the maximum speed for a vehicle is estimated and the vehicles that exceed the stipulated limit are identified as a violation. The core part of the proposed system is license plate recognition. To properly extract the license plate with the best view to proceed with the identification process is another problem that needs to be focused on. We utilized deep neural networks in a novel way for the aforesaid purpose. As these neural networks consist of numerous parameters, we utilized GPU for processing to gain smoothness in real-time. Using our novel segmentation free license plate identification method which utilizes object detection principle to fully capture the speed violation along with its offender. Numerous field trials proved that the proposed methodology provides far superior performance levels compared to the conventional systems and the other existing methodologies, which will certainly cater to the demanding requirements of Transportation 4.0.

scholarly journals An Illegal Dumping Detection System Based on Image Processing in OpenALPR

2021 ◽  
Vol 15 ◽  
pp. 1-7
Author(s):  
Wan Zakiah Wan Ismail
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Detection System ◽  
Social Economy ◽  
Raspberry Pi ◽  
License Plate ◽  
Microwave Radar ◽  
Plate Number ◽  
Illegal Dumping

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

Sign in / Sign up

Export Citation Format

Share Document