scholarly journals Vehicle Vacant Seat Identification and Mask Detection using Image processing

2021 ◽  
Vol 9 (VII) ◽  
pp. 118-121
Author(s):  
Mr. Shubham Ingole
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Face Detection ◽  
Detection Technique ◽  
Processing Technology ◽  
Raspberry Pi ◽  
Human Being ◽  
People Tracking ◽  
Software Application ◽  
The Face

This article describes the technique of real-time face detection, mask detection, and vacant seat available in the vehicle. There are so many technologies for finding seat availability in the vehicle. But image processing technology is very popular today. Face detection is part of image processing. It is used to find the face of a human being in a certain area. Face detection is used in many applications, such as facial recognition, people tracking or photography. In this paper, the face detection technique is used to detect the vacant seat availability in the vehicle and also to detect whether the passenger wear the mask on his face or not. The webcam is installed in the vehicle and connected with the Raspberry Pi 3 model B. When the vehicle leaves the station, the webcam will capture images of the passengers in the seating area. The webcam will be mounted on the vehicle. The images will be adjusted and enhanced to reduce noise made by the software application. The system obtains the maximum number of passengers in the vehicle that processes the images and then calculates the availability of seats in the vehicle. In covid-19 situation mask detection is necessary. so this system also used to detect the mask on face.

scholarly journals Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

scholarly journals Real-Time Multi-Scale Face Detector on Embedded Devices

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2158 ◽  
Author(s):  
Xu Zhao ◽  
Xiaoqing Liang ◽  
Chaoyang Zhao ◽  
Ming Tang ◽  
Jinqiao Wang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Class Imbalance ◽  
Network Capacity ◽  
Raspberry Pi ◽  
Class Imbalance Problem ◽  
Embedded Devices ◽  
Basic Step ◽  
The Face ◽  
Face Detector

Face detection is the basic step in video face analysis and has been studied for many years. However, achieving real-time performance on computation-resource-limited embedded devices still remains an open challenge. To address this problem, in this paper we propose a face detector, EagleEye, which shows a good trade-off between high accuracy and fast speed on the popular embedded device with low computation power (e.g., the Raspberry Pi 3b+). The EagleEye is designed to have low floating-point operations per second (FLOPS) as well as enough capacity, and its accuracy is further improved without adding too much FLOPS. Specifically, we design five strategies for building efficient face detectors with a good balance of accuracy and running speed. The first two strategies help to build a detector with low computation complexity and enough capacity. We use convolution factorization to change traditional convolutions into more sparse depth-wise convolutions to save computation costs and we use successive downsampling convolutions at the beginning of the face detection network. The latter three strategies significantly improve the accuracy of the light-weight detector without adding too much computation costs. We design an efficient context module to utilize context information to benefit the face detection. We also adopt information preserving activation function to increase the network capacity. Finally, we use focal loss to further improve the accuracy by handling the class imbalance problem better. Experiments show that the EagleEye outperforms the other face detectors with the same order of computation costs, on both runtime efficiency and accuracy.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals PEMODELAN SISTEM DETEKSI WAJAH SEBAGAI PENGHITUNG JUMLAH PENUMPANG TRANSPORTASI PUBLIK

2021 ◽  
Vol 4 (1) ◽  
pp. 67-77
Author(s):  
Fransiska Sisilia Mukti ◽  
Lia Farokhah ◽  
Nur Lailatul Aqromi
Keyword(s):  
Embedded System ◽  
System Design ◽  
Face Detection ◽  
Public Transportation ◽  
Detection System ◽  
Raspberry Pi ◽  
The Internet ◽  
Detection Mechanism ◽  
Information System Design ◽  
The Face

Bus is one of public transportation and as the most preferable by Indonesian to support their mobility. The high number of bus traffics then demands the bus management to provide the maximum service for their passenger, in order to gain public trust. Unfortunately, in the reality passenger list’s fraud is often faced by the bus management, there is a mismatch list between the amount of deposits made by bus driver and the number of passengers carried by the bus, and as the result it caused big loss for the Bus management. Automatic Passenger Counting (APC) then as an artificial intelligence program that is considered to cope with the bus management problems. This research carried out an APC technology based on passenger face detection using the Viola-Jones method, which is integrated with an embedded system based on the Internet of Things in the processing and data transmission. To detect passenger images, a webcam is provided that is connected to the Raspberry pi which is then sent to the server via the Internet to be displayed on the website provided. The system database will be updated within a certain period of time, or according to the stop of the bus (the system can be adjusted according to management needs). The system will calculate the number of passengers automatically; the bus management can export passenger data whenever as they want. There are 3 main points in the architecture of modeling system, they are information system design, device architecture design, and face detection mechanism design to calculate the number of passengers. A system design test is carried out to assess the suitability of the system being built with company needs. Then, based on the questionnaire distributed to the respondent, averagely 85.12 % claim that the Face detection system is suitability. The score attained from 4 main aspects including interactivity, aesthetics, layout and personalization

scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

Development of Embedded Intelligent Line-Following Robot Based on Image Processing

2013 ◽  
Vol 849 ◽  
pp. 346-350 ◽  
Author(s):  
Yu Juan Wen ◽  
Zhe Liu
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Technology ◽  
Intelligent Robot ◽  
Real Time Control ◽  
Inspection Robot ◽  
Time Control ◽  
Image Processing Technology ◽  
Line Following

Aiming at dealing with the problems of traditional inspection robot, such as low accuracy of path identification, an embedded intelligent inspection robot based on image processing is developed. With uCOSs efficient real-time control ability and image processing technology, the robot can follow the line automatically. The experiment showed that the intelligent robot can identify the path accurately and adjust the direction and speed quickly

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