OpenCV

One aspect of a growing VR industry that developers have to face is the ethics behind the technology. This includes making sure that technology is readily available and accessible to as large of a population as possible. Current consumer virtual reality (VR) headsets typically utilize two controllers to navigate a virtual environment, leading to accessibility issues for potential users that cannot effectively operate a controller. We propose NaVRgate, a proof of concept idea that removes the need for controllers in which a user uses expressions to navigate a virtual environment. The system utilizes the computer webcam and computer vision face and eye position tracking to capture the nature of expression tracking, with certain positional thresholds representing different facial expressions. To test this system, We design a game environment where a user navigates with either a controller or the face position tracker, collecting a set of orbs scattered around the map as quickly as they can, comparing the efficiency between navigation through the novel computer vision and traditional controller methods. Users are also questioned on the difficulty of use and experience with each control input method. This paper details the process of the development and drafts, to the statistical experiment constructed to determine the efficiency of head gestures.

A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) we employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, (ii) we derive several novel colour-based face invariants, and (iii) on a large database of video sequences we examine and evaluate the largest number of colour-based representations in the literature. Our results suggest that colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images.

in this paper, we explain our Traffic Detection technique using OpenCV concept, Neural Networks, Tensorflow, and how it is successfully detecting and identifying vehicles and other roadside attributes such as pedestrians, signs, and lane markings for a thorough analysis through a road surveillance camera image. Our pre-trained SVM model is highly efficient and accurate in performing the desired task successfully.
The paper should be of interest to readers in the areas of Image processing, neural networks, and machine learning.

Dynamically changing background ("dynamic background") still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: that of swaying tree branches and their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new dataset suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected dataset, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Motorcycle accidents have been hastily growing throughout the years in several countries because road safety is often neglected by riders worldwide leading to accidents and deaths. To address this issue, most countries have laws which mandate the use of helmets for two-wheeler riders so, it is very important for motorcyclists to understand the risks of riding without a helmet. Riders who do not wear helmets are at greatest risk of suffering a traumatic brain injury; if they met with an accident without protection, the head is susceptible to a harrowing impact in an accident. In India, there is a rule that mandate helmet only for riders but not even for passengers. Anyone may suffer from accident or head injuries whom are using motorcycle without helmet. It should be mandatory for everyone to wear helmet; even for children. So, to mandate this we have developed a system which is based on Tensor flow & Keras in the field of Computer Vision. System is able to detect whether motorcyclists wear helmet or not even at real time. If anyone of them is present with no helmet then system will precisely observed the situation and declare the rule violations. The system can be implemented in malls, offices, marts, school and college that only allows people to enter the premises only after detecting helmet with automated barrier. It will definitely affect the use of helmet that will save humans life at all.

Face Recognition begins with extracting the coordinates of features such as width of mouth, width of eyes, pupil, and compare the result with the measurements stored inthe database and return the closest record (facial metrics).Nowadays, there are a lot of face recognition techniques and algorithms found and developed around the world. Facial recognition becomes an interesting research topic. It is proven by numerous number of published papers related with facial recognition including facial feature extraction, facial algorithm improvements, and facial recognition implementations. Main purposes of this research are to get the best facial recognition algorithm (Eigenface and Fisherface) provided by the Open CV 2.4.8 by comparing the ROC (Receiver Operating Characteristics) curve and implement it in the attendance system as the main case study. Based on the experiments, the ROC curve proves that using the current training set, Eigenface achieves better result than Fisherface. Eigenface implemented inside the Attendance System returns between 70% to 90% similarity for genuine face images.

Face recognition Technology is being appealing field in recent years. Taking attendance is a real-world task, which needs a creative solution to reduce time, efforts and resources. Face recognition Attendance is a technique to detect and recognize the students' or employees' face for marking their attendance by using unique face features extracted from the images captured. In proposed face recognition project, a raspberry PI based system will be able to detect and recognize human faces in a quick and accurate way via images or videos that are being captured through a Camera. It detects the faces within the image and compares it with the listed faces in the database. On recognition of a registered face on the captured image assortments, the attendance of that student is marked present otherwise absent. The system is developed on Open Source image processing library hence; it is not hardware nor software dependent. Many algorithms are used to ameliorate the performance of the system but the concept to be implemented here is Eigen matrix concept (Eigen Faces). It is used to convert the images into the matrix, based on the features of the images, to easily recognize the faces of the students, so that the attendance database can be easily updated. I. INTRODUCTION Presently, attendance management is important task in every educational organization. Managing students' attendance during lecture period is time consuming task. The most of the institutions uses pen-paper based approach and some have adopted automated methods such as fingerprint biometric techniques and RFID based attendance System. However, these techniques make students to wait in a queue that depletes time and it is intrusive. Some institutions still use manual attendance approach in which a subject teachers call out the students' name and mark the attendance manually. This approach may be considered as a time-consuming or sometimes it happens for the teacher to miss someone to mark present or students may answer multiple times to make proxy attendance of their friends. So, the problem of accuracy and reliability arise when we think about the traditional process of taking attendance in the classroom. Face recognition technology is one of the least intrusive and fastest growing technology. Face recognition based attendance is an approach to automatically mark the presence or the absence of the student in the classroom by recognizing their faces. It can also be implemented in the exam sessions to ensure the presence of the real student who has registered for exam. It works by identification of humans using the most unique characteristics of their faces via images captured through camera, so it becomes highly reliable for the machine to mark the presence of all the students available within the room. The concept of this paper is aimed towards developing a less intrusive, economical and more efficient automated student attendance managing system using face recognition. II. EXISTING METHODS Some systems exist in automated attendance technique. However, only a few are enforced implementing a less intrusive approach. Some existing systems include Finger print based attendance, Iris based attendance and RFID based attendance. In this research, my focus is on face recognition and a cost effective architecture for its implementation. Face recognition based attendance system with raspberry pi 3A+ using Eigen faces algorithm has been proposed. In the work, a camera is placed at top position of the class that cover whole class which is interfaced with a raspberry pi 3A+ module for capturing students entering the class. The images are stored in the raspberry pi 3A+. The raspberry pi 3A+ module is used to achieve high speed of operation.

Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the relationship between facial features and a driver's drowsy state, variables that reflect facial features have been established. In this paper, we proposed a method for extracting detailed features of the eyes, the mouth, and positions of the head using OpenCV and Dlib library in order to estimate a driver's level of drowsiness.

Road traffic accidents are being recognised as a major problem in developing countries. A mechanism to prevent these road accidents is the need of the hour. It is hoped that the mechanism which we introduced can prevent accidents occurring due to overtaking. In order to accomplish the task, we use a camera which is fixed on the rear view mirror of our vehicle which collects the footage of the opposite lane up to 100m. This footage is used to detect any obstacle present using real time computer vision. If an obstacle is present, driver will be alarmed so. This will ensure overtaking only at safe conditions

DETECCIÓN DE ROSTROS EN IMAGENES, CON Python y "opencv". Es sabido que una de las posibilidades que nos ofrece la librería "opencv" es la de detectar distintos tipos de elementos en una imagen, vídeo, grabación.., mediante el uso de clasificadores, los cuales, consisten, basicamente en archivos "xml", que contienen el dataset que se encargará de ir analizando cada región de la imagen, y determinando (mediante sucesivas pruebas) si la información de dicha región se corresponde con el elemento a detectar. Así, existen, ya creados, clasificadores, diseñados específicamente, para detectar elementos muy variados (personas, ojos, manos, vehículos, matriculas, animales…etc). En esta ocasión vamos a aplicar un clasificador orientado a la detección de rostros humanos en una imagen, el cual aplicaremos sobre un archivo llamado "abba.png", que mostramos a continuación:

Motorcycle accidents have been hastily growing throughout the years in several countries because road safety is often neglected by riders worldwide leading to accidents and deaths. To address this issue, most countries have laws which mandate the use of helmets for two-wheeler riders so, it is very important for motorcyclists to understand the risks of riding without a helmet. Riders who do not wear helmets are at greatest risk of suffering a traumatic brain injury; if they met with an accident without protection, the head is susceptible to a harrowing impact in an accident. In India, there is a rule that mandate helmet only for riders but not even for passengers. Anyone may suffer from accident or head injuries whom are using motorcycle without helmet. It should be mandatory for everyone to wear helmet; even for children. So, to mandate this we have developed a system which is based on Tensor flow & Keras in the field of Computer Vision. System is able to detect whether motorcyclists wear helmet or not even at real time. If anyone of them is present with no helmet then system will precisely observed the situation and declare the rule violations. The system can be implemented in malls, offices, marts, school and college that only allows people to enter the premises only after detecting helmet with automated barrier. It will definitely affect the use of helmet that will save humans life at all.

Using a neural convolution network, this research presents an automatic system for recognising age and gender based on a person's face. In recent years, one of the most effective research subjects has been collecting information from a person's face. The human face has facial features such as eyes, ears, nose, chin, and other aspects that can be examined based on our requirements. The detection of age and gender begins with facial recognition. Detection is the process of identifying many features based on a single input. Our system makes use of OpenCV and includes a few deep convolutional emotional networks that have been trained to detect faces from inserted images and predict their age and gender using either a webcam or an image. Not only are deep neural convolutional networks costly, but they are also difficult to implement.

The area of face recognition can help to understand the emotional states of user, along with the intensity of those emotions at that particular time, emotion is detected by using feature extraction and process the data to detect the intensity of the emotion.The objective of this paper is to recognaize the intensity of emotions and find out its percentage, for which, a real live video is preperded, Accordingly to the individaul expression in the live video, the emotions are catgorsized in to angry, disgust, scared, happy, sad, surprised, and neutral, emotion intensity is also calculated in real time.

Surveillance is important in most of the applications. Vehicle number plate detection is the major part of traffic surveillance. The detection of number plates has become more challenging and interesting in the past few years. The most challenging part of number plate detection is the varying size, shape and font styles of the number plates. The interesting part of number plate detection is its use in security applications. This project proposes a method to detect the vehicle number plate using methods like edge detection and morphological operations. The approach is performed in five steps. The first step is image acquisition which captures the image scene using a camera. Next step is preprocessing which involves conversion of an image to a different model and noise reduction. The next step is license plate detection which involves use of various edge detection algorithms. The final two steps are character recognition and character matching which involves knn classifier and finally the characters are compared with test samples and matched.

Authentication is that the basic issue in the field of computer primarily based communication. Face recognition is widely utilized in many applications reminiscent of system security and door system. The proposed work describes the way to take student’s act victimization face recognition .The face recognition is enforced with the help of Camera and Open CV formula. The system will acknowledge the face of specific student and saves the response in information automatically. The system additionally includes the feature of retrieving the list of students who are absent during a explicit day. The various information is recorded with the assistance of a camera connected as a part of front of the classroom which is able to be continuously taking footage of students, detect the faces in image and it distinguishes appearances alongside the information and mark the attendance. This work initial audit the connected works in the field of participation administration conjointly the face acknowledgment. At that time, it presents our framework structure and plan. Finally, the experiments area unit enforced and it shows the advance of the performance of the attendance system. In this work using Raspberry pi and is employed to find the face with the assistance of OpenCv.

This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy.

This project and experiment were conducted with the aim of utilizing the human hands as an object to operate computers. It is intended to support and use technologies in the field of contactless shopping/payments.
The program is developed by using python programming language with the help of additional libraries such as OpenCV.
In order to use this program, the person needs to be in front of a computer webcam. The webcam will be used to recognize the shape and the pattern of the presenters’ hands. The program will display results of recognized hand patterns of gestures on a live video frame stream. The result of this project is a program that can be used for improve the user experience of contactless systems and make safer transactions in a time of global pandemic of 2020, where social distancing is one of the main things to consider.

in this paper, we explain our Traffic Detection technique using OpenCV concept, Neural Networks, Tensorflow, and how it is successfully detecting and identifying vehicles and other roadside attributes such as pedestrians, signs, and lane markings for a thorough analysis through a road surveillance camera image. Our pre-trained SVM model is highly efficient and accurate in performing the desired task successfully. The paper should be of interest to readers in the areas of Image processing, neural networks, and machine learning.

The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving detection accuracy while supporting a real-time operation by applying YOLOv3, which is the most representative of one-stage detectors, with redesigning the loss function. In addition, by using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3-tiny, the proposed algorithm, YOLOv3, improves the mean average precision (mAP). Nevertheless, the proposed algorithm is capable of real-time detection faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications.
Keywords: object detection, YOLOv3, real-time systems, keras, deep learning

Face recognition systems are used in practically every industry in this digital age. One of the most widely utilized biometrics is face recognition. It can be used for security, authentication, and identity, among other things. Despite its low accuracy relative to iris and fingerprint identification, it is extensively utilized because it is a contactless and non-invasive technique. Face recognition systems can also be used to track attendance in schools, colleges, and companies. Because the existing manual attendance system is time consuming and difficult to maintain, this system intends to create a class attendance system that employs the concept of face recognition. There's also the possibility of proxy attendance. As a result, the demand for this system grows. Database development, face detection, face recognition, and attendance updating are the four steps of this system. The photos of the kids in class are used to generate the database. Faces are discovered and recognized from the classroom's live streaming footage. At the end of the session, the attendance will be mailed to the appropriate faculty.

Accurate Object Detection was always a big deal and an important part of the Information Technology era. After the arrival of Machine Learning and Deep Learning technologies, the efficiency and accuracy for Object Detection increased significantly. These technologies greatly assisted in the evolution of the computer vision systems. This project focuses to integrate state-of-the-art technique for object detection with the aim of the achieving high accuracy. In this project we are using a deep learning part which is Tensorflow and OpenCV, which is a library of programming functions mainly aimed at real-time computer vision. There are few other libraries used which helped in object detection to make the system more accurate and reliable in the long run. We trained the network on various objects which are ordinary and easily available in the market. This projects aim to reduce the billing time in super markets with fast and accurate detection.

This paper presents a recognition system, which can be helpful for a blind person. Hand gesture recognition system and face recognition system has been implemented in this paper using which various tasks can be performed. Dynamic images are being taken from a dynamic video and is being processed according to certain algorithms. In the Hand gesture system Skin color detection has been done in YCbCr color space and to discover hand convex defect character point of hand is used where different features like fingertips, angle between fingers are being extracted. According to gesture Recognized, various tasks can be performed like turning on the fan or lights. While in face recognition, Haar Cascade Classifiers and LBPH recognizer are being used for face detection and recognition respectively. With the help of OpenCV, The research has been implemented. Various hand gestures and human faces have been detected and identified using this system. The hand gesture was recognized with an accuracy of 95.2% was achieved and facial recognition was done with an accuracy of 92%.

Face mask detection involves in detection the placement of the face then crucial whether or not it's a mask thereon or not. the problem is proximately cognate to general object notion to detect the categories of objects. Face identification flatly deals with identifying a particular cluster of entities i.e., Face. it's varied applications, like autonomous driving, education, police work, and so on. This paper presents a simplified approach to serve the above purpose using the basic Machine Learning (ML) packages such as TensorFlow, Keras, OpenCV and Scikit-Learn. The planned technique detects the face from the image properly and so identifies if it's a mask on that or not. As an investigation task performing artist, it ought to conjointly sight a face at the side of a mask in motion. The technique perform accuracy up to 95.77% and 94.58% respectively on two different datasets and count optimized values of parameters using the Sequential Convolutional Neural Network model to detect the presence of masks correctly without causing over-fitting.

This file include the program code and its explanations. the process: web-cam capturing in real time, changing the color to gray scale, smoothing medianBlur, normlized histugram, pixel with the maximum value and the one with minimum value and their locations, edges detection canny algorithm, minimum and maximum threshold based on the value of the pixel with maximum value in the image. this method will be self adjustable, thresholding, adaptive threshold algorithm, simple threshold algorithm, face detecting Face detector cascade haarcascade.

This research introduces a novel method for controlling mouse movement with a real-time camera. Adding more buttons or repositioning the mouse's tracking ball are two common ways. Instead, we recommend that the hardware be redesigned. Our idea is to employ a camera and computer vision technologies to manage mouse tasks (clicking and scrolling), and we demonstrate how it can do all that existing mouse devices can. This project demonstrates how to construct a mouse control system. I.

DETECCIÓN DE CONTORNOS EN IMÁGENES CON PYTHON Y "opencv". El propósito del presente trabajo, es exponer el procedimiento a seguir, para la detección de contornos sobre una imagen con el lenguaje de programación "Python", haciendo uso de la librería "opencv". Así, para empezar, procederemos a importar las librerías y recursos que vamos a emplear en nuestro proyecto: >>> #IMPORTAMOS LIBRERÍAS A USAR. >>> import cv2 >>> import matplotlib.pyplot as plt >>> Haciendo uso conjunto de estas dos librerías, nos disponemos a representar los contornos de una imagen sobre fondo blanco, contenida en una archivo de nombre "hand.png":

This file include the program code and its explanations.
the process:
web-cam capturing in real time, changing the color to gray scale, smoothing medianBlur,  normlized histugram, pixel with the maximum value and the one with minimum value and their locations, edges detection canny algorithm, minimum and maximum threshold based on the value of the pixel with maximum value in the image. this method will be self adjustable, thresholding, adaptive threshold algorithm, simple threshold algorithm, face detecting  Face detector cascade haarcascade.

The microcontrollers have many applications in industry, education and our lives; many electronic devices that we use during a day are manufactured of microcontrollers and most of our daily tasks are impossible without microcontrollers, we study the design and manufacture of LPC2368 and A20 hardware in this thesis.
One of the most important science in medicine, robotics and industrial is image processing which has many applications, we are going to implement computer free image processing in microcontrollers and face detection by A20, and finally writing an image processing library for all microcontrollers which has been tested for LPC2368 and LPC1768.
One of the most interesting and complex science in computer field is image processing which is used to improve performance of processing, each part of a job will be run parallel in processors, we’ll use LPC2368 and A20 (which has dual core ) to do image processing.



در تنظیم و تایپ پایان نامه وقت و فرصت کمی داشته ام ، و مطالب ممکن است خلاصه باشند ، سعی بر آن دارم که به مرور زمان آن را تکمیل نمایم .
پیشنهادات و نظرات خود را حتما ارسال نمائید.
مطالب زیر در این پایان نامه پوشش داده شده اند :
1. آموزش میکروکنترلر LPC2368
2. آموزش راه اندازی چندین واحد داخلی LPC2368
3. کار با ماژول های زیر :
1- LED
2- 7-Seg
3- Keypad
4- Graphical LCD (TFT LCD , N96)
5- RFID Reader : EM18
6- Bluetooth : HC-05 , HC-06
7- GPS Module : ublox 5 , ublox6
8- SD Card Module
9- GSM Module : SIM 900a , SIm 908
4. پردازش تصویر
5. آموزش مقدماتی پردازش تصویر با OpenCv
6. مقدمات پردازش تصویر و انجام پردازش تصویر سطح پایین بدون استفاده از کتابخانه های پردازش تصویر و پیاده سازی آن ها در C#
7. انجام پردازش تصویر سطح پایین در میکروکنترلر LPC2368 و نوشتن کتابخانه پردازش تصویر که می توان با کمی تغییرات آنرا در سایر میکروکنترلرها استفاده نمود
8. راه اندازی و کار با CubieBoard 2
9. کار با لینوکس در Cubie Board 2 و نحوه راه اندازی دوربین و کدنویسی به زبان های c ، c++ و python در لینوکس
10. پردازش تصویر با CubieBoard 2  و OpenCv و تشخیص چهره

بزودی مطالب را برای lpc1768 نیز آماده خواهم نمود

Multiple people detection in real-time is still a challenging task despite having different techniques. It is challenging because partially occluded people are still often not recognized in a heavily populated area, and also due to Non-Maximum suppression, correct bounding boxes are also discarded, which leads to imprecision in the detections. This paper presents the various modifications done to multiple people detection and tracking algorithms, which improves the efficiency and accuracy of the previously used cases.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.