scholarly journals RANCANG BANGUN APLIKASI NEW NORMAL COVID-19 DETEKSI PENGGUNAAN MASKER MENGGUNAKAN HAAR CASCADE CLASSIFIER

2021 ◽  
Vol 15 (2) ◽  
pp. 199-209
Author(s):  
Ade chandra Saputra ◽  
Ahmadi Ahmadi ◽  
Ariesta Lestari
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Programming Language ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Public Places ◽  
The Face ◽  
Haar Cascade ◽  
Analysis Design

During the COVID-19 pandemic, when in public places, it is required to apply the 4M health protocol, namely wearing masks, washing hands, maintaining distance, and avoiding crowds. In its implementation, there are officers who always maintain and remind people not to violate health protocols. Like remembering to wear a mask. The mask detection application is made as a computerized surveillance system that can store images of violations of the use of masks and provide warning sounds. Observations, discussions and literature studies are sources of data in this empirical research. Using Python as a programming language assisted with OpenCV for image processing. After passing through the 4 stages of Waterfall, namely Analysis, Design, Manufacturing and Development and Testing, an application is produced where the Raspberry Pi is a processing tool and images are captured from the camera module with a resolution of 1080x1024 px. This application can detect the use of masks with an accuracy of 90.5% using the Machine Learning Haar Cascade Classifier method. Where the condition of the face is a maximum of 30 degrees turned to the side and looked up

scholarly journals Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

2020 ◽  
Author(s):  
Kusworo ADI ◽  
Catur Edi WIDODO ◽  
Aris Puji WIDODO ◽  
Hilda Nurul ARISTIA
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
High Accuracy ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
The Face ◽  
Haar Cascade ◽  
Two Parameters ◽  
Significant Factors

Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver. Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected. Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%. Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

scholarly journals Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone

2018 ◽  
Vol 197 ◽  
pp. 11008 ◽  
Author(s):  
Asep Najmurrokhman ◽  
Kusnandar Kusnandar ◽  
Arief Budiman Krama ◽  
Esmeralda Contessa Djamal ◽  
Robbi Rahim
Keyword(s):  
Face Recognition ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Security Issues ◽  
Access System ◽  
The Face ◽  
Access Control System ◽  
Haar Cascade ◽  
Door Lock ◽  
Access Rights

Security issues are an important part of everyday life. A vital link in security chain is the identification of users who will enter the room. This paper describes the prototype of a secured room access control system based on face recognition. The system comprises a webcam to detect faces and a solenoid door lock for accessing the room. Every user detected by the webcam will be checked for compatibility with the database in the system. If the user has access rights then the solenoid door lock will open and the user can enter the room. Otherwise, the data will be sent to the master user via Android-based smartphone that installed certain applications. If the user is recognized by the master user, then the solenoid door lock will be opened through the signal sent from the smartphone. However, if the user is not recognized, then the buzzer will alert. The main control circuit on this system is Raspberry pi. The software used is OpenCV Library which is useful to display and process the image produced by webcam. In this paper, we employ Haar Cascade Classifier in an image processing of user face to render the face detection with high accuracy.

scholarly journals THE CALCULATION SYSTEM OF NUMBER OF PEOPLE IN A ROOM BASED ON HUMAN DETECTION USING HAAR-CASCADE CLASSIFIER

2021 ◽  
Vol 2 (2) ◽  
pp. 75-84
Author(s):  
Gusti Ngurah Rama Putra Atmaja ◽  
Koredianto Usman ◽  
Muhammad Ary Murti
Keyword(s):  
Image Processing ◽  
Computation Time ◽  
Scale Factor ◽  
Human Detection ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Final Project ◽  
Object Distance ◽  
Haar Cascade ◽  
Commercial Applications

Data of number of people in the room, calculations are usually carried out by assigning someone to oversee a room. In this final project, a system for calculating the number of people in the room is designed with image processing based on human detection that can be used in rooms, both for commercial applications and for security. This system uses Raspberry Pi device that already has an image processing method Haar-Cascade Classifier.   Input data is in the form of video taken directly via webcam to be captured into a frame so that it   can be used as a input the Haar-Cascade Classifier method and perform the counting process will be sent to the Antares platform. The system design has been tested with five scenarios. Scenario 1 the effect of the distance of the object, scenario 2 the effect of the pose of the object, scenario 3 the effect of the amount the object in the frame, scenario 4 affects the scale factor and scenario 5 measurement computation time. Scenarios 1 to 3 will do the best configuration for minimum neighbour. The system gets the best accuracy of 98,5% when the object distance 4 meters, the best accuracy of 96,6% when the object is facing forward and accuracy the best is 97,7% when the object in the frame is more than two objects with the best configuration use the minimum neighbour 5. Scenario 4 gets accuracy the best is 76,2% when using the scale factor 1.1. Scenario 5 gets the average computation time of the system is under one second, meaning the detection process done pretty fast.

scholarly journals Real time face recognition of video surveillance system using haar cascade classifier

2021 ◽  
Vol 21 (3) ◽  
pp. 1389
Author(s):  
Adlan Hakim Ahmad ◽  
Sharifah Saon ◽  
Abd Kadir Mahamad ◽  
Cahyo Darujati ◽  
Sri Wiwoho Mudjanarko ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Video Surveillance System ◽  
Cascade Classifier ◽  
Camera System ◽  
Surveillance Camera ◽  
Haar Cascade

<div>This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.</div>

scholarly journals Pengolahan Citra Digital untuk Keyboard Virtual Sebagai Antarmuka pada Aplikasi Berbasis Web

2015 ◽  
Vol 5 (2) ◽  
pp. 111
Author(s):  
Raksaka Indra Alhaqq ◽  
Agus Harjoko
Keyword(s):  
Image Processing ◽  
Web Application ◽  
Index Finger ◽  
Processing Technology ◽  
White Background ◽  
Cascade Classifier ◽  
Virtual Keyboard ◽  
Image Processing Technology ◽  
Optimal Distance ◽  
Haar Cascade

AbstrakSejak pertama kali komputer ditemukan, keyboard selalu menjadi alat utama yang menjadi penghubung interaksi antara manusia dan komputer. Saat ini banyak komputer yang menerapkan teknologi pengolahan citra untuk menjadikannya perantara interaksi antara komputer dan manusia.Dalam penelitian ini, penulis mencoba untuk menerapkan teknologi pengolahan citra yang digunakan untuk keyboard virtual pada aplikasi web. Digunakan webcam untuk menangkap citra ujung jari telunjuk. Hasil capture citra akan dikirimkan ke server localhost untuk diproses dengan image processing. Untuk mendeteksi ujung jari telunjuk, digunakan metode Haar Cascade Classifier. Proses pendeteksian tersebut menghasilkan koordinat yang akan dikirimkan ke aplikasi web yang selanjutnya dijadikan acuan untuk menentukan posisi tombol pada keyboard virtual. Sehingga keyboard virtual akan menampilan karakter sesuai dengan yang ditunjuk oleh ujung jari telunjuk.Dari hasil pengujian yang dilakukan, jarak optimal ujung jari telunjuk dengan webcam adalah 20 – 35 cm. Derajat kemiringan ujung jari telunjuk untuk dapat terdeteksi antara 0° – 10°. Sistem mampu mengenali ujung jari telunjuk pada ruangan berlatar belakang putih polos dan terdapat sedikit perabot. Waktu respon untuk menampilkan karakter keyboard virtual rata-rata 5,156 detik. Sehingga keyboard virtual pada sistem ini belum mampu dijadikan antarmuka pada aplikasi web, dikarenakan masih sulit digunakan dalam mengarahkan ujung jari telunjuk ke tombol karakter yang diinginkan.Kata kunci—aplikasi web, Haar Cascade Classifier, keyboard virtual, pengolahan citra  AbstractSince the first computer was founded, keyboard is always been a primary tool for interaction between humans and computers. Today, many computers use image processing technology to make interaction between computers and humans.The author try to apply image processing technology that implemented to virtual keyboard on web application. Using a webcam to capture the tip of index finger and the results will be sent to the localhost server for processing with image processing. Using Haar Cascade Classifier method to detect the tip of index finger, it will produce coordinates that sent to the web application and it used as a reference for determining button positions on virtual keyboard. Virtual keyboard characters will display after appointed by the tip of  index finger.From testing results, optimal distance from index finger to webcam is 20 – 35 cm. System can recognize the tip of index finger on white background and room with few furnitures. Average response time for displaying virtual keyboard sentences is 3 minutes and 28.838 seconds. So the virtual keyboard on this system was not able to be used as interface on web application, because it difficult to use in directing the tip of index finger to the character keys.Keywords—web application, Haar Cascade Classifier, virtual keyboard, image processing

scholarly journals PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER

2018 ◽  
Vol 1 (3) ◽  
pp. 92
Author(s):  
Kadek Oki Sanjaya ◽  
Gede Indrawan ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
Image Processing ◽  
Detection System ◽  
High Accuracy ◽  
Special Study ◽  
Cascade Classifier ◽  
Integral Image ◽  
Main Method ◽  
Average Accuracy ◽  
Haar Features ◽  
Haar Cascade

Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

2020 ◽  
Author(s):  
Shrirang Ambaji Kulkarni ◽  
Varadraj P. Gurupur ◽  
Steven L. Fernandes
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Raspberry Pi

An Image Retrieval Method Based on Machine Learning and SVM

2014 ◽  
Vol 631-632 ◽  
pp. 474-477
Author(s):  
Hui Yun Xiong ◽  
Juan Zhao
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Integral Method ◽  
Sample Pretreatment ◽  
Cascade Classifier ◽  
Fixed Size ◽  
Retrieval Method ◽  
Cascade Algorithm ◽  
Svm Algorithm ◽  
The Face

Image recognition has been a research hotspot in the field of machine learning; this paper puts forward a kind of cascade algorithm based on SVM and AdaBoost. The algorithm to select the sample pretreatment, fixed size of window image segmentation into different areas, then using Haar - like rectangular figure characteristics of integral method for feature extraction, finally using AdaBoost cascade classifier to classify the SVM training. Through the face recognition experiments show AdaBoost cascade of SVM algorithm improve the classification accuracy, error rate get reduced obviously.

scholarly journals Smart Parking Guidance System Using 360o Camera and Haar-Cascade Classifier on IoT System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 864-872 ◽  
Keyword(s):  
Video Processing ◽  
Guidance System ◽  
Raspberry Pi ◽  
Parking Space ◽  
Shopping Malls ◽  
Cascade Classifier ◽  
Parking System ◽  
Parking Lot ◽  
Smart Parking ◽  
Haar Cascade

Nowadays, smart parking guidance system is a crucial research for people’s convenience. The main objective of this research is to develop and analyze on a smart parking guidance system where current available system was compared to this new proposed system. Limited parking space has become serious issue since the number of Malaysia’s populations who are using car keep increasing. Some of the big companies, shopping malls and other public facilities already deployed a smart parking system on their building. However, there are still a lot of buildings that do not own it because the system required a lot of investment, where the huge parking areas need higher cost to install sensors on each parking lot available. The proposed smart parking guidance system in this research was depending on a 360° camera that was modified on raspberry pi camera module and 360o lens and Haar-Cascade classifier. The image and video processing was by Open CV and python program to detect the available parking space and cloud firebase was used to update data where users can access the parking space availability by android mobile phone specifically at a closed parking space. A single 360°camera was replaced several sensors and camera which was implemented on traditional smart parking system. An analysis was done on the performance of the system where it can detect the parking availability with 99.74% accuracy and which is far better than conventional system including reliability and cost for the parking space guidance system.

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