OpenCV Research Articles

Machine Learning Biometric System using Viola Jones Algorithm

Abstract: A person's identity is revealed through their face. The method utilised to employ this physical attribute has undergone a major alteration since the introduction of image processing technologies.. Taking attendance is a crucial daily duty in many educational and professional settings. The professor calls the student's name and records the attendance in the conventional manner. It takes a lot of time to physically take attendance in a classroom, which is a very vital responsibility. It is frequently carried out manually by shouting out names or roll numbers. To expedite this process and improve time management, a Face identification-based attendance system is offered. This system will automate the time-consuming procedure by collecting student photographs, names, roll numbers, classes, and sections. A spreadsheet database that is updated hourly also contains the guide's name. Processing is performed using a web camera, and student photographs are extracted from Open CV. Using a pre-defined dataset and the Viola Jones technique, automatic attendance detection can be accomplished. The system keeps an Excel file with the most recent instructor data that is updated hourly.

Sudoku Solving Algorithm and Grid Based Models for Digit Recognition

Abstract: In recent information technology advances, computer vision has been exalted. Due to its broad range of uses and features, it has gained attention for resolving difficult problems in the real world that are beyond the capacity of human intellect. This marvel of artificial intelligence becomes practical and convenient when it is applied to everyday life. Sudoku has been a common source of entertainment in many articles in newspapers, magazines, and applications. The conclusion of this research is to use computer vision techniques to solve the Sudoku puzzle. We used some well-known tools, including Open CV, OCR, and Tensorflow. The strategy we utilised to employ these tools to solve the Sudoku puzzle is described in the paper. The paper's technique includes a number of stages, including.

ATM Based Face Recognitions System

Abstract: Automated Teller Machines are widely used nowadays by people. But it’s hard to carry their ATM card everywhere, people may forget to have their ATM card or forget their PIN number. The ATM card may get damaged and users can have a situation where they can’t get access to their money. In our proposal, use of biometrics for authentication instead of PIN and ATMcard is encouraged. Here, The Face ID is preferred to high priority, as the combination of these biometrics proved to be the best among the identification and verification techniques. The implementation of ATM machines comes with the issue of being accessed by illegitimate users with valid authentication code. The users are verified by comparing the image taken in front of the ATM machine, to the images which are present in the. If the user is legitimate the new image is used to train the model for further accuracy. This system uses open CV to process the image being obtained and Hear Cascade Classifier to detect the faces in the image. The face recognitions done using Local Binary Pattern.

Research Paper on ATM Based on Face Recognitions System

Abstract: Automated Teller Machines are widely used nowadays by people. But it’s hard to carry their ATM card everywhere, people may forget to have their ATM card or forget their PIN number. The ATM card may get damaged and users can have a situation where they can’tget access to their money. In our proposal, use of biometrics for authentication instead of PIN and ATM card is encouraged. Here, The Face ID is preferred to high priority, as the combination of these biometrics proved to be the best among the identification and verification techniques. The implementation of ATM machines comes with the issue of being accessed by illegitimate users with valid authentication code. The users are verified by comparing the image taken in front of the ATM machine, to the images which are present in the. If the user is legitimate the new image is used to train the model for further accuracy. Thissystem uses open CV to process the image being obtained and Hear Cascade Classifier to detect the faces in the image. The face recognitions done using Local Binary Pattern.

Face Recognition Based Attendance System

In colleges, universities, organizations, schools, and offices, taking attendance is an important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one making our task easy. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, section, and photographs, is trained and is also available for teachers and staff attendance. The images are extracted using Open CV a library of python. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

AI & ML Powered Autonomous Drone System

The use of drone technology is growing in popularity and has many different industrial uses. Drones are used in agriculture for pesticide and fertilizer application, crop monitoring, and mapping. Drones can be used in the building sector to check job sites and track development. In the movie industry, drones are also utilized for aerial photography and in search and rescue efforts to reach inaccessible places. In general, drone technology has the potential to revolutionize a variety of industries and offer fresh approaches to problems that were previously challenging to solve. Our idea is to develop AI ML based autonomous drone system that can be deployed in many areas for multiple applications such as surveillance, warehouse management etc. The layout includes a PI-CAM V2 camera on a quadcopter using GPS and system powered by Robot Operating System, Open CV and other technologies that gives ability to drone to fly without need of pilot autonomously. Key Word: Multipurpose, Robot Operating System, Simulation, Surveillance, Tracking, Quadcopter, OpenCV, Machine Learning, Object Detection.

Two Way Sign Language for Deaf and Dumb using Deep Convolution Neural Network

Abstract: The goal is to build a bridge between the hearing and hearing-impaired communities and to start two-way communication. Here we propose a two-way sign language translator that translates speech in real time into sign language and vice versa. Each video frame is processed using Python's Open CV Library. Additionally, we use the Gaussian Mixture-based Background/Foreground Segmentation Algorithm to remove the background from each frame. The hearing impaired community will then be more effectively integrated into regular schools thanks to this technology, making education more affordable and available to them.

Blind People Helping Hand

Abstract: Blind people are facing many problems in their day-to-day life. They find it very difficult to read books as well as to recognize the object in front of them. From the survey of WHO,30 million peoples are permanently blind and 285 billion peoples with vision impairment. if you notice them, you can very well know about it they can’t walk without the help of other. one has to ask guidance to reach their destination. they have to face more struggles in their life daily life. Visually impaired people find difficulties detecting obstacles in front of them, during walking in the street, which makes it dangerous. A lots of blind people suffer in their own lives because of their vision loss. Vision is one of the five important senses in the human body. People with Vision loss have their own disability. Many countries around the world provide special assistance to these needed people to improve their life quality as good as possible. They provide them with special equipment for their disability to improve their daily life like voice message service, electronic stick that guide them while moving around and other specialized equipment. Blind people can't even walk without any aid. Many times they rely on others for help. Several technologies for the assistance of visually impaired people have been developed. This paper proposes a system for blind people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted by the user. Its functionality addresses the identification of objects .This will help the visually impaired person to manage day-to-day activities through his/her surroundings. To help the blind people the visual world has to be transformed into the audio world with the potential to inform them about objects. Therefore, we propose to aid the visually impaired by introducing a system that is most feasible, compact, and cost effective. So, we implied a system that makes use of Raspberry Pi. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform. The system consists of a webcam interfaced with raspberry pi. Pi Camera detects and find the type of object with the help of ultrasonic sensor. Ultrasonic sensor to detect the real time hurdles while walking on the roads. The ultrasonic sensor used in this project plays a vital role. It detects the object in front of this. When object is detected a indication sound is given to the user via earphone. While they hear this sound they can know an obstacle in front of them. The system is intended to provide Raspberry Pi. The proposed system detects an object around them and sends feedback in the form of speech, warning messages via earphone. This new system may solve some of major problems of blind persons that are still existing. The aim of all these systems is to help the user to detect object without the help of a second person. This system is to provide a low cost and efficient obstacle detection aid for blind which gives a sense of artificial vision by providing information about the environmental scenario of static and dynamic object around them, so that they can walk independently. This paper combines the functionality of Object detection. The objective is to develop user friendly application

Advance Intelligent Video Surveillance System Using OpenCV

In today's society, video surveillance for observing a particular area which includes hospitals, establishments, public parks, and buildings has become a need, where in each person desires to keep their belongings safe and relaxed. The growth in the urban population has ended in an increase in crime. For citizens, video surveillance has had a tremendous influence. Closed-circuit television (CCTV) is the most extensively used machine, but it's miles extra expensive and makes use of more strength and a garage. To address this trouble, we developed a complicated wise video surveillance device for places where human presence is abnormal. It isn't important to constantly screen the area with cameras in such situations. This consumes both the electricity and the storage space required for the footage. Open CV, a device gaining knowledge of software library, is used to put in force this gadget. The proposed gadget operates in such a manner that it captures video then processes frame by frame and starts recording whilst it detects human presence. The surveillance device can be activated if the cameras hit upon any movement. The counseled system gathers information and stores it in a neighborhood database. The video that becomes captured and stored can be utilized to identify the intruder and aid in his seizure. It could be useful in places where human presence is irregular, which includes bank vaults and houses

Automatic Face Recognition Based on Attendance System

In colleges, universities, organizations, schools and offices, attendance is one of the most important daily tasks. In most cases, this is done manually, for example by calling by name or roll number. The main goal of this project is to create an attendance system based on facial recognition that turns this manual process into an automatic process. This project fulfills the requirements to modernize attendance management and time management criteria. This device is installed in the classroom where student information such as name, roll number, class, second and photos are stored. The images are selected with an open CV. Before the start of the respective lesson, the student can approach the machine that will take pictures and compare them with the approved data set. The ESP32CAM was used as the camera and processing board in this project. The image is processed and the algorithm, histogram data is compared with the approved data set and the device marks the presence automatically. An Excel spreadsheet will be developed in class and updated with information from each class teacher. Key Word: ESP32 Cam; Face Detection; FTDI Chip; Automatic Attendance Marking; Quality of the image.

OBJECT DETECTION AND TEXT TO SPEECH CONVERSION BASED ON YOLOV7 USING DEEP LEARNING

Abstract—Object detection is a computer vision technique that locates objects in images or videos by creating bounding boxes around them. In this paper, we propose a model based on object detection using deep learning technologies along with text to speech conversion.An object detection system uses a deep learningmodel to detect objects using YOLO (You Only Look Once) and text-to- speech (TTS) to synthesize a voice announcement about each object. The system we used is built using python OpenCV tool and Google text to speech (gTTS) is used to convert text into audio segment. First variations of YOLO algorithm are compared and then the best one is used according to result we get it by training it on COCO dataset. After the object is detected, the name of the detected object is displayed then the voice output is generated by using Google Text To Speech(gTTS) module. The contribution we make is to present a visual substitution system that uses features extraction and matching to recognize objects with a voice feedback. Index Terms—Object Detection, YOLO, Open CV, python, Google Text To Speech

Performance Evaluation of Virtual Mouse Using AI

This paper proposes a way of controlling the position of the mouse cursor with the help of a fingertip without using any electronic device. We can perform the operations like clicking and dragging objects easily with help of different hand gestures. The only necessary input device for the suggested system is a webcam. The software will need to be implemented using Python and OpenCV. The output of the webcam will be shown on the computer's screen so that the user can fine-tune it. NumPy, Autopy, and Media pipe are the Python requirements that will be utilised to create this system. Key Word: Open Cv, Autopy, Mediapipe Numpy; Calibrated, Gesture.

SIGN LANGUAGE INTERPRETER

Every average person has the ability to see, hear, and respond to their surroundings. Some unfortunate people are without this significant blessing. These people primarily include the deaf and dumb, who interact with others by using sign language. Yet, since not all common people can understand their sign language, communicating with regular people is a significant limitation for them. Also, this will make it difficult for members of the deaf and dumb communities to engage with others, especially when they want to participate in educational, social, and professional settings. The goals of this research are to create a sign language translation system for hearing or speech challenged individuals utilising deep learning and neural networks to help those who are deaf or have trouble speaking or hearing communicate with others. The model utilised in this classification assignment is a pretty simple Convolutional Neural Network implementation for the methodology (CNN). This exploits the convolutional property, which was primarily developed for the analysis of visual imagery. Three layered CNNs were trained and tested in real time using segmented RGB hand motions. With the help of a personal device, such as a laptop webcam, simple photos of the hand were used to construct the image dataset for each gesture. I was able to get training accuracy of roughly 89% and testing accuracy of 98.5% with this CNN model. Keywords : Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Gesture Recognition, Open CV, TensorFlow

Object Surveillance Detection

Abstract: Thefts have increased over the past few years. This creates a dangerous climate where people live in fear. Issues with home security are a worry in the current atmosphere. The current generation of intrusion detection systems is very expensive and prone to false alarms. Open CV and mobile devices are used to find a solution to this issue. Intruders can be precisely detected using this framework, which filters the motion of moving objects. An API by the name of Twilio will notify the user when an intrusion is made, and the video will be saved locally. This application's main purpose is to act as a surveillance system that can identify persons, recognise faces, and display user information. This lowers the possibility of future system penetration by people. This project gathers camera photos and using image comparison methods to identify intruders using the Open CV library. The streamed video will be delivered from the server to the remote administrator via the Android phone if an intrusion is found after comparison. The administrator can then take the necessary steps and locally notify her security. Users are notified by MMS, SMS, or email when notifications are sent and pertinent data is recorded. able to display videos relevant to the user. This approach improves social stability, keeps your house secure, and lowers burglaries.

Currency Detection and Recognition System Based on Deep Learning

-This paper suggests a method for recognizing currency through image analysis. The method relies on the three characteristics of color, height, and texture, which utilized for distinction. The method put forth in this document can be used to find the currencies of numerous nations. For the purposes of execution, only paper currencies from India are taken into account. This system, which employs the CNN (Convolution Neural Network), makes it simpler to check currency anywhere, at any moment. Every denomination of India was used in our testing, and the system was able to identify with 95% precision. To improve the technique's accuracy, a classification model is created using all of the previously stated factors. Features of paper money are crucial to this identification. Key Words: Currency recognition, color, CNN, Open CV, ResNet.

The Deep Learning based Smart Navigational Stick for Blind People

Blind and visually impaired people find difficulty in detecting obstacles and recognizing people in their way, which makes it dangerous for them to walk, to work, or to go in a crowded area/place. They have to be cautious all the time to move, while avoiding any solid obstacles in their way. Typically, they use different aid devices to reach their destination or to accomplish their daily task. The normal stick is useless for blind and visually impaired people since it cannot detect barriers or people's faces. Visually impaired individuals are unable to distinguish between different types of objects in front of them. They are unable to gauge the size of an object or its distance from them. Several works have been done by public individuals and scientific investigators but their work is dearth in technological aspect. This technological aspect need to be addressed by adding artificial intelligence (AI). This prototype aims to help blind and visually impaired individuals in several aspects to simply obtain/perform everyday tasks and help these individuals to live with the same confidence as sighted people live.Therefore, this study inclined deep learning Mobile-Net Single Shot MultiBox detection (SSD) algorithm for object recognition and Dlib library for face recognition. Subsequently, the proposed solution is using an Open CV and Python. Additionally, Ultrasonic sensors are used for distance measurement, which can be a great help for visually impaired people. These components are grouped together to work effectively and efficiently for the development of visually impaired people. The recognition procedure was revealed through headphones, which notifies the visually impaired when face or any object get recognized. Inclusively, the innovative solution would be a great aid for the blind and visually impaired individuals. As a result, to test and validate the accuracy of the smart navigational stick, several experiments have been conducted on a range of objects and faces. Hence, this study’s modified navigational system was adequate and valid for visually impaired people.

Human Face Detection and Recognition from the Video Using Deep Learning

Attendance management can be a significant burden on teachers if done manually. To solve this problem, It is proposed that we use an intelligent and automatic presence management system. Using this framework, the problem of proxies and tagged students present while they are not physically present can easily be resolved. This system marks the attendance using live video stream. The frames are extracted from video using Open CV. The main implementation stages used in this type of system are detection and recognition of the detected face, for which dlib is used. After this, the connection of the acknowledged faces should be conceivable by comparing with the database containing the faces of the students. It will be an effective technique to manage student attendance..

INFANT MONITORING SYSTEM USING RASPBERRY PI

In today’s situation, parents are busy in their career. As in India that both the parents need to work and look after their infants, so more workload and stress is there on such families especially on female counterparts. Therefore, the chance of providing better infant care is reduced. This may cause many troubles to the health of children. For this, temperature, moisture, motion and sound sensors. So that the system can monitor the external conditions like increasing body temperature, crying of the baby when the voice exceeds the pre- determined range, movements of the baby, when found to be moving continuously and also indicates when the diaper is needed to be changed, if excess wet was observed. This system is attached with a video camera which is operated based on the instructions from the raspberry pi pico and is used to capture the video when the motion sensor detects the baby's movements continuously. The video will be displayed on the screen to monitor the baby. Key Words: PIR sensor, sound sensor, moisture sensor, image processing, open cv, blynk app

Cyan Fluorescent Carbon Quantum Dots with Amino Derivatives for the Visual Detection of Copper (II) Cations in Sea Water

Amino- and carboxyl-functionalized carbon quantum dots (Amino-CQDs) were synthesized through fast and simple microwave treatment of a citric acid, ethylenediamine and ethylenediaminetetraacetic acid (EDTA) mix. The reproducible and stable optical properties from newly synthesized CQD dispersion with a maximum absorbance spectra at 330 nm and the symmetric emission maximum at 470 nm made the Amino-CQDs a promising fluorescence material for analytical applications. The highly aminated and chelate moieties on the CQDs was appropriate for a copper (Cu2+) cation sensor in the linear range from 1 × 10-4 mg/mL to 10 mg/mL with a limit of detection at 0.00036 mg/mL by static fluorescence quenching effects. Furthermore, Amino-CQDs demonstrated stable fluorescence parameters for assays in diluted alkali metal solution (Na+ and K+) and sea water. Finally, a visual sensor, based on Amino-CQDs, was successfully created for the 0.01-100 mg/mL range to produce a colorimetric effect that can be registered by computer vision software (Open CV Python).

Critical Evaluation of Architectural Rudiments from the Perspective of Religion

India owns 60% of the world's second-largest agricultural land area. Around 10% to 40% of the crops are impacted by birds. We'll attempt to find an answer to the following questions in this project. Our main objective is to repel the birds without hurting them, so we are driven to complete the task without using any human effort. We have selected one crop and analyzed. There are four types of birds that are harming the paddy, according to the survey that was done. The peacock, house sparrow, pegion and grey patridge have the biggest effects as compared to other species. For machine learning and digital image processing, we use the ORB algorithm approach. The primary library employed is the open CV. The Pictures of birds entering the agriculture field is captured and compared with the above four species. If the result is positive then the signal is transmitted to the microcontroller. Then the microcontroller functions the necessary hardware to repel the birds. Key Words: Birds, ORB Algorithm, Affecting birds, Features of image.