Computer Vision Library Research Articles

Hill Climb Game Play with Webcam Using OpenCV

Abstract: Computer vision is a process by which we can understand how the images and videos are stored and manipulated, also it helps in the process of retrieving data from either images or videos. Computer Vision is part of Artificial Intelligence. Computer-Vision plays a major role in Autonomous cars, Object detections, robotics, object tracking, etc. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. It comes with a highly improved deep learning (dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. This does allow us to take our models trained using dedicated deep learning libraries/tools and then efficiently use them directly inside our OpenCV scripts. MediaPipe is a framework mainly used for building audio, video, or any time series data. With the help of the MediaPipe framework, we can build very impressive pipelines for different media processing functions like Multi-hand Tracking, Face Detection, Object Detection and Tracking, etc.

Review of Facial Recognition Techniques

Abstract: Facial Recognition, the biggest breakthrough in Biometric identification and security since fingerprints, uses an individual’s facial features to identify and recognize them. A technology that seems too farfetched taken straight from a science fiction novel is now available in smartphones in the palm of our hands. Facial Recognition has gained traction as the primary method of identification whether its mobile phones, smart security systems, ID verification or something as simple as login in a website. Recent strides in facial recognition technologies have made it possible to design, build and implement a facial recognition system ourself. Using Computer Vision and machine learning libraries like Facial Recognition and Dlib, we can create a robust system that can detect faces and then match and identify it with a database of pre-loaded facial data to successfully recognize them. This study conducted a literature review of these aforementioned technologies and various other advancements in the field of computer vision facial recognition by other scholars in their research papers. This paper analyzes domains to understand the working of these machine learning models and their different implementations in facial recognition systems. The research conducted by us during this review will be paramount in creating a proof-of-concept prototype facial recognition system. Keywords: DLib, Facial _Recognition, Machine Learning (ML), Deep Learning (DL), CNN, KNN, Face Detection, HOG, Support Vector Machine (SVM), Face Recognition.

OpenCV Using on a Single Board Computer for Incorrect Facemask-Wearing Detection and Capturing

OpenCV (Open Source Computer Vision Library) is a software library intended for real-time dynamic image processing, created by Intel. In this study, the library will be used to detect the face, nose and mouth. Furthermore, the system is equipped with the knowledge that if the mouth and nose or one of them is detected, then the face has not used the mask correctly and the system records the face. The system is supported by an image capture device in the form of a camcorder, a processing device in the form of a single board computer, such as a Raspberry Pi and a display device in the form of a monitor. And the result is that the system is able to make a decision whether the face is wearing a mask correctly or not. By means of labeling around the face in the form of red angular lines, if not properly use the mask. The success rate is 88.4% using detector parameters, namely: scale factor = 1.1 for all face, nose and mouth object libraries.

РАСПОЗНАВАНИЕ ПОКАЗАНИЙ АНАЛОГОВЫХ ПРИБОРОВ НА ПРИМЕРЕ АВИАЦИОННЫХ ПРИБОРОВ ВЫСОТНО-СКОРОСТНОЙ ГРУППЫ

The article considers the issue of analog instrument reading using the example of high-altitude and high-speed flight instruments. The article provides a detailed description of the software that can read analog gauge data through the example of flight instruments during their verification. The article describes the main functions of computer vision library, image processing, and general-purpose numerical algorithms with open source (OpenCV), used for instrument readings. The software is developed in the high-level universal programming language Python 3.7.7. The results of the program are presented, showing this method performance of checking high altitude flight instruments.

Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos

COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosed in December 2019 in China. As of 25th Aug 2021, there are 165 million confirmed COVID-19 positive cases and 4.4 million deaths globally. As of today, though there are approved COVID-19 vaccine candidates only 4 billion doses have been administered. Until 100% of the population is safe, no one is safe. Even though these vaccines can provide protection against getting seriously ill and dying from the disease, it does not provide 100% protection from getting infected and passing it on to others. The more the virus spreads; it has more opportunity to mutate. So, it is mandatory to follow all precautions like maintaining social distance, wearing mask, washing hands frequently irrespective of whether a person is vaccinated or not. To prevent spread of the virus, contact tracing based on social distance also becomes equally important. The work proposes a solution that can help with contact tracing/identification, knowing the infected persons recent travel history (even within the city) for few days before being assessed positive. While the person would be able to give the known contacts with whom he/she has interacted with, he/she will not be aware of who all were in proximity if he/she had been in public places. The proposed solution is to get the CCTV (Closed-Circuit Television) video clips from those public places for the specific date and time and identify the people who were in proximity—i.e., not followed the safe distance to the infected person. The approach uses YOLO V3 (You Only Look Once) which uses darknet framework for people detection. Once the infected person is located from the video frames, the distance from that person to the other people in the frame is found, to check if there is a violation of social distance guideline. If there is, then the people violating the distance are extracted and identified using Facial detection and recognition algorithms. Two different solutions for Face detection and Recognition are implemented and results compared—Dlib based models and OpenCV (Open Source Computer Vision Library) based models. The solutions were studied for two different CCTV footages and the results for Dlib based models were better than OpenCV based models for the studied videos.

Design of UAV Cooperative Target Tracking System Based on Computer Vision Algorithm

In this paper, a video intelligent monitoring and tracking system is designed based on Computer Vision algorithm. The system is developed and implemented with OpenCV computer vision library, Visual Studio and MFC framework. Firstly, the overall framework of the system is designed, and the second is to analyze the preprocessing of video data. Then, the target is extracted by the Gaussian mixture background difference method, and the target is separated from the background by thresholding. Then, the target is tracked by Camshift and Kalman filters and an alarm prompt is issued. The final system implementation results show that the moving target extraction based on the Gaussian mixture background difference method, and through binary processing, can be accurately and reliably separated from the background, and the target detection effect is remarkable; the alarm mechanism can be triggered accurately and timely, and the alarm prompt information can be issued.

Face mask detection with temperature check

The coronavirus (COVID-19) pandemic is causing a worldwide health catastrophe, so according to the World Health Organization (WHO), wearing masks in public is an effective safety method. The COVID-19 pandemic has forced governments around the world to impose quarantines to prevent transmission of the virus. According to reports, wearing masks in public does reduce the threat of transmission of the virus. An efficient and cost-effective way to use Artificial Intelligence (AI) to create a secure environment in a manufacturing environment. A hybrid model for using a deep and classic face mask detection device will be proposed. The face mask detection dataset includes the mask, and without mask photos, it uses the Open-Source Computer Vision Library (OpenCV) to detect faces in real-time from the stay circulation through the webcam. It uses the dataset to build a computer vision COVID-19 face mask detector using Python, OpenCV, TensorFlow, and Keras. Using computer vision and deep learning, the goal is to understand whether a character in a picture or video stream is wearing a mask or not using computer vision and deep learning.

Autonomous Streaming Space Objects Detection Based on a Remote Optical System

Traditional image processing techniques provide sustainable efficiency in the astrometry of deep space objects and in applied problems of determining the parameters of artificial satellite orbits. But the speed of the computing architecture and the functions of small optical systems are rapidly developing thus contribute to the use of a dynamic video stream for detecting and initializing space objects. The purpose of this paper is to automate the processing of optical measurement data during detecting space objects and numerical methods for the initial orbit determination.This article provided the implementation of a low-cost autonomous optical system for detecting of space objects with remote control elements. The basic algorithm model had developed and tested within the framework of remote control of a simplified optical system based on a Raspberry Pi 4 single-board computer with a modular camera. Under laboratory conditions, the satellite trajectory had simulated for an initial assessment of the compiled algorithmic modules of the computer vision library OpenCV.Based on the simulation results, dynamic detection of the International Space Station in real-time from the observation site with coordinates longitude 25o41′49″ East, latitude 53o52′36″ North in the interval 00:54:00–00:54:30 17.07.2021 (UTC + 03:00) had performed. The video processing result of the pass had demonstrated in the form of centroid coordinates of the International Space Station in the image plane with a timestamps interval of which is 0.2 s.This approach provides an autonomous raw data extraction of a space object for numerical methods for the initial determination of its orbit.

АЛГОРИТМ ТОЧНОЙ ПОСАДКИ МУЛЬТИРОТОРНЫХ БЕСПИЛОТНЫХ АВИАЦИОННЫХ СИСТЕМ НА СТАНЦИЮ АВТОНОМНОЙ ЗАРЯДКИ

In this article, we propose an algorithm for accurately landing multirotor (quadcopters, hexacopters, etc.) unmanned aerial vehicles (UAVs) at an autonomous charging station. This article also presents methods for locating the charging station and landing the UAV at night. Section 1 describes the general sequential landing procedures. Section 2 describes methods for detecting the ArUco marker and evaluating its position and orientation using the OpenCV computer vision library and shows the recognition result. In section 3, the precise landing algorithm is analyzed in detail, and a block diagram of the algorithm is given. Section 4 discusses the integration of the night vision camera into the landing algorithm.

Development of the computer vision system based on machine learning for educational purposes

The article provides an overview of the origins and current state of machine vision systems, examples of machine vision problems. The article describes the use of computer vision systems in education in both conventional and pandemic conditions. The COVID-19 pandemic has triggered changes in education that have modified existing educational applications of computer vision systems and spawned new ones, including social distancing, facial mask recognition, detection of infiltration into universities and schools, prevention of vandalism and detection of suspicious objects, attendance monitoring, recognition of emotions on faces in and without masks. Computer vision systems can also be used in education to introduce immersive educational resources. On the basis of the analysis of autonomous libraries for the identification of dynamic objects, it is concluded that in the creation of computer vision systems for educational purposes it is advisable to use computer vision libraries based on in-depth learning (in particular, the implementation of convolutional neural networks). A prototype computer vision system developed on the basis of Microsoft Cognitive Toolkit and deployed in the Microsoft Azure cloud is described. The system allows you to perform with a high degree of reliability the main functions: identification of emotions and the presence of a mask on the face, as well as allows you to determine sex, age, hair color, smile intensity, the presence of makeup, glasses, etc.

Phenopype: A phenotyping pipeline for Python

Abstract Digital images are an intuitive way to capture, store and analyse organismal phenotypes. Many biologists are taking images to collect high‐dimensional phenotypic information from specimens to investigate complex ecological, evolutionary and developmental phenomena, such as relationships between trait diversity and ecosystem function, multivariate natural selection or developmental plasticity. As a consequence, images are being collected at ever‐increasing rates, but extraction of the contained phenotypic information poses a veritable analytical bottleneck. phenopype is a high‐throughput phenotyping pipeline for the programming language Python that aims at alleviating this bottleneck. The package facilitates immediate extraction of high‐dimensional phenotypic data from digital images with low levels of background noise and complexity. At the core, phenopype provides functions for rapid signal processing‐based image preprocessing and segmentation, data extraction, as well as visualization and data export. This functionality is provided by wrapping low‐level computer vision libraries (such as OpenCV) into accessible functions to facilitate scientific image analysis. In addition, phenopype provides a project management ecosystem to streamline data collection and to increase reproducibility. phenopype offers two different workflows that support users during different stages of scientific image analysis. The low‐throughput workflow uses regular Python syntax and has greater flexibility at the cost of reproducibility, which is suitable for prototyping during the initial stages of a research project. The high‐throughput workflow allows users to specify and store image‐specific settings for analysis in human‐readable YAML format, and then execute all functions in one step by means of an interactive parser. This approach facilitates rapid program‐user interactions during batch processing, and greatly increases scientific reproducibility. Overall, phenopype intends to make the features of powerful but technically involved low‐level CV libraries available to biologists with little or no Python coding experience. Therefore, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection. Furthermore, image annotations produced by phenopype can be used as training data, thus presenting a stepping stone towards the application of deep learning architectures.

A Multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN

With the development of deep learning, convolutional neural networks (CNN) have been gradually used in pipeline defeats detection. However, due to the complex environment inside the pipeline, few defeat images are not enough for the training of CNN. A multi-defect detection system based on StyleGAN-SDM and fusion CNN for sewer pipelines is proposed in this paper. First, aiming at the problem of data acquisition and small data volume, raw images are preprocessed by StyleGAN-SDM, which integrates StyleGAN v2 and sharpness discrimination model (SDM) to generate multi-defect images and automatically select clear images. The indexes of Inception-Residual score (IRS), accuracy and macro-F1 score to evaluate the quality of the images generated are 2.968 ± 0.024, 99.64%, and 0.997, respectively. Second, to improve the detection accuracy, a multi-defect classification model (MDCM) based on fusion CNN, which combines Inception network and Residual network, is proposed to classify the on-site images into four categories. Third, compared with conventional deep-learning methods, the mean accuracy and macro-F1 score of the proposed model reach 95.64% and 0.955, which are increased by 1.51% and 0.015 by StyleGAN-SDM, respectively. Finally, to solve the timeliness problem of on-site detection, a real-time multi-defeat detection system for sewer pipelines is established with the computer vision library of OpenCV. Some on-site videos are detected with the mean speed of 24.11 FPS and these results could aid the staff.

Application of a computer vision system for recognizing tomato fruits and determining their position relative to the gripper device of the harvesting robot

The article presents a method for recognizing tomato fruits covered with foliage, de-termining their centers and boundaries using the OpenCV computer vision library and a hardware complex based on Raspberry Pi 4. The methods for solving the inverse kinematics problem for the five-link robotic manipulator designed by the authors, installed on a mobile plat-form, in order to create a robot for collecting fruits are considered. The simulation of the manipulator movement in the Scilab environment is performed.

Research on Character Computer Intelligent Recognition of Automobile Front Bar Bracket Based on Machine Vision

Aiming at the problem that the characters in the superimposed character area on the surface of the front bumper bracket of the car are difficult to recognize, a machine vision-based automobile front bumper bracket character recognition method is proposed, use Python, OpenCV and Halcon computer vision library for software system design. For eight images collected from different angles of light source directions, an innovative method of polynomial fitting gray value can reducethe uneven illumination of the images. The photometric stereo algorithm is used to obtain a high-contrast character image, and the separation of the two types of characters is simultaneously achieved. After the image filtering, opening and closing processes canremove background interference, use a morphological improvement algorithm based on scanning algorithm to complete character positioning, and then the improved algorithm of projection dichotomy based on the connected domain size is used to complete the character segmentation, and finally the support vector machine is used for character feature recognition. The experimental results claim that these methods can quickly separate superimposed characters and complete the recognition of non-color difference convex characters and inkjet characters, and the average recognition accuracy rate is over 96%, which meets the expected recognition requirements.

Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

Abstract: Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

RESEARCH ON TARGET RECOGNITION OF COMBAT ROBOT BASED ON OPENCV

With the wide application of robots in various fields of human life, the research on improving the level of intelligent robot is highly concerned by domestic and foreign scholars. As an important part of intelligent robot, robot vision has been paid more and more attention in recent years. Computer vision technology is the use of image processing technology to make the camera and the human eye to identify, judge, feature detection, tracking and other visual functions. The computer vision system is to create a complete artificial intelligence system which can get the needed information in the plane image or the three-dimensional image data. Opencv plays an important role in the field of computer vision which is a cross platform computer vision library based on open source. The main purpose of this study is: According to the feature recognition and matching of the object, make the camera of the combat robot has the vision through a detection system based on OpenCV visual library to detect and track the moving objects in the line of sight

Design of Micro-Expression Recognition System Based on Computer Cloud Computing

<p style="text-align: justify;"><span style="font-family: "times new roman";">This is an era of big data, and the rapid growth of data is no longer an era that can be processed perfectly by a single machine in the past. In order to adapt to the data processing needs of the times, micro-expression recognition, as an old subject of pattern recognition, also needs to make new changes. In this case, this paper designs a micro-expression recognition system based on computer cloud computing. First of all, this paper studies the research status and application value of micro-expression recognition, and then this paper analyzes the MapReduca algorithm, the micro-expression recognition algorithm and the OpenCV computer vision library in detail. Then, this paper uses SVM-Adaboost algorithm and NSMD algorithm to realize the MapReduce micro-expression recognition system. Finally, this article has carried on the experimental test to the recognition effect and recognition time of SVM-Adaboost algorithm and NSMD algorithm, and draws relevant conclusions.</span>

Emotion based video player

One's work can be done efficiently only if their mood is good. Emotions is the index of mood. Here model capture one's image as an input, predict their mood and play a video of opposite genre as an output, in order to change their mood, which is the main goal of this project. Hence taking them through an emotional roller coaster. The solution makes use of CNN (convolutional neutral networks) for detecting one's mood. It uses OpenCV (open-source computer vision library) in-order to get user's image using their respective web camera. It is done by importing modules like web-browser and requests in-order to get access to YouTube to play videos accordingly. The average accuracy rate of the system has increased to 98.53 percent. Eight primary emotion classes have been effectively classified by the method. As a result, the proposed strategy has been shown to be effective in recognizing emotions.

Research on Face Recognition Based on Improved Convolutional Neural Network Using Raspberry Pi

In this paper, a face recognition system has been designed using Raspberry Pi based on convolution neural network technology. Convolution neural network that using OpenCV (open source computer vision library) to realize real-time face recognition and feature extraction. The difficult and important problem in deep learning network is that it could be over fitting very easily. In order to alleviate the over fitting phenomenon and improve the accuracy of face recognition, the dropout technology of convolutional neural network had been employed to improve the recognition accuracy and ensure the robustness of face recognition. The results of experiments show that our methods has the advantages in the computer time and recognition rate for the low construction cost, simple structure and strong portability. The accuracy of face detection for static and dynamic images using Raspberry Pi is above 85% in our testament.

MyelTracer: A Semi-Automated Software for Myelin g-Ratio Quantification.

In the central and peripheral nervous systems, the myelin sheath promotes neuronal signal transduction. The thickness of the myelin sheath changes during development and in disease conditions like multiple sclerosis. Such changes are routinely detected using electron microscopy through g-ratio quantification. While g-ratio is one of the most critical measurements in myelin studies, a major drawback is that g-ratio quantification is extremely laborious and time-consuming. Here, we report the development and validation of MyelTracer, an installable, stand-alone software for semi-automated g-ratio quantification based on the Open Computer Vision Library (OpenCV). Compared with manual g-ratio quantification, using MyelTracer produces consistent results across multiple tissues and animal ages, as well as in remyelination after optic nerve crush, and reduces total quantification time by 40-60%. With g-ratio measurements via MyelTracer, a known hypomyelination phenotype can be detected in a Williams syndrome mouse model. MyelTracer is easy to use and freely available for Windows and Mac OS X (https://github.com/HarrisonAllen/MyelTracer).