scholarly journals Automatic Number Plate Recognition System using Raspberry Pi

2020 ◽  
Vol 9 (2) ◽  
pp. 1863-1865
Keyword(s):  
Computer Vision ◽  
Access Control ◽  
Embedded System ◽  
Recognition System ◽  
Binary Image ◽  
Security System ◽  
Raspberry Pi ◽  
Gray Scale

Automatic Number Plate Recognition System is an embedded system that acknowledges the vehicle number plate automatically. Automatic Number Plate Recognition is a technology for computer vision to find the number plates of vehicles from the images. There are many applications like parking, access control, security system, etc. In this paper, we propose a technique of implementing Automatic Number Plate Recognition System using Python and Open Computer Vision Library. The different stages that are involved in the implementation are conversion into gray scale, conversion into binary image, detects the edges of the image, to find the contours and finally displays the number plate of a vehicle

Developing Algorithms for a Berry Recognition System Used in Robotized Harvesting of Garden Strawberry

2020 ◽  
Vol 67 (1) ◽  
pp. 133-141
Author(s):  
Dmitriy O. Khort ◽  
Aleksei I. Kutyrev ◽  
Igor G. Smirnov ◽  
Rostislav A. Filippov ◽  
Roman V. Vershinin
Keyword(s):  
Computer Vision ◽  
Technological Process ◽  
Degrees Of Freedom ◽  
Computer Control ◽  
Recognition System ◽  
Raspberry Pi ◽  
Laser Sensors ◽  
Lighting Conditions ◽  
Systems Research ◽  
Degree Of Maturity

Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.

scholarly journals IoT based facial recognition door access control home security system using raspberry pi

2020 ◽  
Vol 11 (1) ◽  
pp. 417
Author(s):  
Syafeeza Ahmad Radzi ◽  
M.K. Mohd Fitri Alif ◽  
Y. Nursyifaa Athirah ◽  
A. S. Jaafar ◽  
A. H. Norihan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Security System ◽  
Raspberry Pi ◽  
Small Computer ◽  
Home Security ◽  
Learning Technique ◽  
Access Control System ◽  
Computer Board ◽  
Internet Of Thing

The home security system has become vital for every house. Previously, most doors can be open by using traditional ways, such as keys, security cards, password or pattern. However, incidents such as a key loss has led to much worrying cases such as robbery and identity fraud. This has become a significant issue. To overcome this problem, face recognition using deep learning technique was introduced and Internet of Thing (IoT) also been used to perform efficient door access control system. Raspberry Pi is a programmable small computer board and used as the main controller for face recognition, youth system and locking system. The camera is used to capture images of the person in front of the door. IoT system enables the user to control the door access.

scholarly journals Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

2020 ◽  
Vol 8 (3) ◽  
pp. 210-216
Author(s):  
Subiyanto Subiyanto ◽  
Dina Priliyana ◽  
Moh. Eki Riyadani ◽  
Nur Iksan ◽  
Hari Wibawanto
Keyword(s):  
Face Recognition ◽  
Smart Home ◽  
Principal Component ◽  
Recognition System ◽  
Classification Performance ◽  
Security System ◽  
Raspberry Pi ◽  
Home Security ◽  
Ga Algorithm ◽  
The Face

Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.

scholarly journals Face recognition for automatic vehicle ignition based on Raspberry Pi

2020 ◽  
Vol 1 (2) ◽  
pp. 53-68
Author(s):  
Alex V. Nuñez ◽  
Liliana N. Nuñez
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Facial Recognition ◽  
Data Gathering ◽  
Software Component ◽  
Security System ◽  
Raspberry Pi ◽  
Hardware Platform ◽  
And Training

In this project a facial recognition application for automatic vehicle ignition is developed. This application is built using a Raspberry Pi as the hardware platform and the OpenCV library for computer vision as the software component. In this research the different methods for automobile security are analyzed, as well as, the different methods used to perform face recognition.  The main goal of this application is to enhance the security system of the vehicle, allowing to ignite the vehicle only by register users. To achieve this goal three main processes are carried out, face detection, data gathering, and training the system to grant access through face recognition.

scholarly journals A Smart Watchdog - Intruder Detection System

2018 ◽  
Vol 7 (3.34) ◽  
pp. 231
Author(s):  
P Vasuki ◽  
Sesu Priya A ◽  
Soundarya R
Keyword(s):  
Embedded System ◽  
Detection System ◽  
Vital Role ◽  
Security System ◽  
Research Centre ◽  
Raspberry Pi ◽  
Security Systems ◽  
Security Controls ◽  
Dc Power ◽  
The Individual

In todays world, Security is a matter of great concern. Security controls play a vital role in protecting resources from espionage, sabotage, damage and theft. Our proposed system is to develop a security system with improved facilities, which tries to eliminate the limitations posed by the existing security systems. The current manual security system depends mostly on human involvement, which is prone to error, and the security is concentrated only at the front door which requires subjects cooperation. To solve these issues we have proposed a Smart Watchdog System. The system watches the environment, and if there is a human activity, the system captures it. The system automatically detects faces of the individual from the activity using firmware. We have planned to maintain the database of authorised inmates and workers of a place and verifies of every individual arriver. This feature enables the system to automatically recognises the unauthorised users and gives an alert when it encounters entry of unauthorised users even without the human assistance. The system also detects the unauthorised entry in the mass. The entire system is planned to be ported to Raspberry-Pi based Embedded System supported with DC power back up. This method can be employed in ladies hostels as well as to the secured places like the data centre, atomic research centre and military where the unauthorised entry is restricted.

scholarly journals An Agile Security System for Automobiles using IoT

2020 ◽  
Vol 9 (1) ◽  
pp. 1502-1504
Keyword(s):  
Embedded System ◽  
Liquid Crystal Display ◽  
Pressure Sensors ◽  
Security System ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Motion Sensors ◽  
Efficient System ◽  
Central Processing

Thieves are becoming smarter day-by-day which results in increase of looting of automobiles like scooters, cars and many other. To overcome this problem there is a crucial need for an effective system that diagnoses the vehicle theft. In this paper, an IoT based agile security system by using Raspberry Pi as the central processing unit of the entire system, a lightweight, cheap and efficient system is researched, built and explored. The Linux Embedded System gathers the data from Passive Infra-Red (PIR) motion sensors, pressure sensors, gas sensors, Global Positioning System (GPS), Pi camera, buzzer, and Liquid Crystal Display (LCD). The system has generally 2 modes. They are: Owner mode and Theft mode. If the system detects any intrusion in the vehicle it gives an alarm on detection, capture the image of the person by using image processing technique and identifies who is trying to unlock the vehicle and send coordinates of the vehicle when the intruder opens the vehicle door and starts moving the car, along with images of intruder to the owner by using a GSM module. By using GPS module, we can be to get the latitude and longitude of the vehicle remotely when the intruder has theft the vehicle.

scholarly journals Face recognition using viola-jones depending on python

2020 ◽  
Vol 20 (3) ◽  
pp. 1513
Author(s):  
Khansaa Dheyaa Ismael ◽  
Stanciu Irina
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Facial Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Security System ◽  
Software System ◽  
Human Face ◽  
Smart Buildings ◽  
Smart Building

<p>In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:</p><p>Eigenfaces → createEigenFaceRecognizer()</p><p>Fisherfaces → createFisherFaceRecognizer()</p><p>Local Binary Patterns Histograms → createLBPHFaceRecognizer()</p>Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.<p> </p>

scholarly journals Security System to Prevent Tailgating Using Face Recognition and Ultrasonic Sensor

2020 ◽  
Vol 2 (5) ◽  
pp. 20-25
Author(s):  
Kannamma R ◽  
Bhargavi S ◽  
Bhavani Sree S ◽  
Mahalakshmi J
Keyword(s):  
Embedded System ◽  
Ultrasonic Sensor ◽  
Security System ◽  
Raspberry Pi ◽  
Security Guards ◽  
The People ◽  
Cloud Database ◽  
The One ◽  
Portable Embedded System ◽  
Python Package

Tailgating is the one where an employee holds the office door for others to enter into the building with one access. This leads to insecure where in the unknown person can also enters into the building with the access of the original employee. To overcome this, introducing security system that prevents tailgating that provides authentication, accuracy, flexibility and gives more convenience to the security guards. It is an embedded based system and built under the Linux environment. First the faces of all the people is captured and trained using OpenCV python package for the purpose of further experimentation in the future. In this Raspberry pi is used as a main controller along with camera which enables to access image processing with any portable embedded system. When the person enters near to the gate, the ultrasonic sensor starts to sense and triggers the camera which detects the person face and checks with the trained dataset using the Haar Cascading algorithm, if it matches the gate gets opened. If suppose the person enters the gate with other person with one access control, then again, the camera gets triggered to capture the unauthorized person face and sends the mail of the detected person to the concerned authority through firebase cloud database.

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