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

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.

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

Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ◽

10.47111/jti.v15i2.3291 ◽

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

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Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

Iranian Journal of Public Health ◽

10.18502/ijph.v49i9.4084 ◽

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.

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

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.7634 ◽

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

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

International Journal of Natural Science and Engineering ◽

10.23887/ijnse.v1i3.12938 ◽

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

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

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1142.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 864-872 ◽

Cited By ~ 2

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

MATEC Web of Conferences ◽

10.1051/matecconf/201819711008 ◽

2018 ◽

Vol 197 ◽

pp. 11008 ◽

Cited By ~ 2

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.

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Pendeteksi Wajah Secara Real Time pada 2 Degree of Freedom (DOF) Kepala Robot Menggunakan Deep Integral Image Cascade

CYCLOTRON ◽

10.30651/cl.v3i1.4306 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Dwi Agung Ayubi ◽

Dwi Arman Prasetya ◽

Irfan Mujahidin

Keyword(s):

Real Time ◽

Face Detection ◽

Humanoid Robot ◽

Degree Of Freedom ◽

Raspberry Pi ◽

Cascade Classifier ◽

Integral Image ◽

Haar Cascade ◽

Time Accuracy ◽

Face Detector

Abstrak— Teknologi Robot merupakan karya terbaik yang sangat penting bagi kehidupan manusia modern saat ini untuk mempermudah semua pekerjaan manusia. Perkembangan dunia robot saat ini akan difokuskan pada robot yang memiliki fitur mirip manusia. Bahkan diharapkan memiliki kemampuan berinteraksi dan berperilaku seperti manusia yaitu robot humanoid, mekanisme dari gerakan robot humanoid memiliki derajat kebebasan Degree of Freedom (DOF). Layaknya pada manusia robot diberi kemampuan penglihatan untuk mendeteksi adanya objek yang ditangkap secara real time Penelitian kepala robot 2 degree of freedom (DOF) untuk pendeteksi wajah secara real time menggunakan metode Deep Integral Image Cascade untuk deteksi wajahnya. Untuk keakurasian pendeteksi wajah dengan real time pada penelitian ini dengan pengujian akurasi terbesar adalah 95,25% dengan waktu respons pendeteksi tercepat 7 detik dengan waktu terlama 8,55 second rata-rata data citra semuanya tidak terdeteksi dengan benarKata kunci: Raspberry pi, Pendeteksi wajah, Degree of freedom, Haar cascade classifier, Robot kepalaAbstract— Robot technology is the best work that is very important for modern human life today to facilitate all human work. The development of the robot world today will be focused on being a robot that has human-like features. Even expected to have the ability to interact and behave like a humanoid robot, the mechanism of humanoid robot movement has a degree of freedom of Degree of Freedom (DOF). Like in the robot man is given the ability of vision to detect the presence of objects captured in real time robotic head Research 2 degree of freedom (DOF) for face detection in real time using the Deep Integral Image Cascade method to Face Detection. For the real-time accuracy of the face detector in this research with the greatest precision testing is 95.25% with the fastest detection response time of 7 seconds with the oldest time 8.55 second the average image data everything is not detected with Really.Keywords: Raspberry Pi, face detector, Degree of freedom, Haar Cascade classifier, Robot head

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Metode Haar-Cascade Classification Menggunakan Raspberry Pi

MIND Journal ◽

10.26760/mindjournal.v2i2.13-22 ◽

2018 ◽

Vol 2 (2) ◽

pp. 13-22

Author(s):

Muhammad Ichwan ◽

Milda Gustiana Husada ◽

Aldri Helmaputra

Keyword(s):

Raspberry Pi ◽

Cascade Classifier ◽

Integral Image ◽

Traffic Light ◽

Haar Cascade

Proses untuk mendeteksi objek dalam citra manggabungkan empat kunci utama yaitu Haar like feature, Integral Image, Adaboost learning dan Cascade Classifier. Metode haar-cascade classification dapat dimanfaatkan sebagai alternatif dalam pengaturan traffic light. Cara kerjanya yaitu, citra yang telah di-capture akan dicocokan dengan data latih, apabila dalam citra tersebut terdapat objek yang sesuai dengan data latih maka objek tersebut di tandai, sehingga objek akan terhitung jumlahnya dengan cara melewati sebuah garis virtual yang dibuat sistem. Dari hasil jumlah objek terdeteksi, sistem dapat mengatur berapa lama lampu merah dan hijau menyala atau mati sesuai dengan jumlah objek yang telah ditentukan. Oleh karena itu, alternatif pengaturan traffic light menggunakan metode Haar-cascade classification digunakan untuk pendeteksian objek. Hasil dari penelitian menggunakan kamera 18 megapiksel 60 FPS jumlah mobil terhitung di garis lebih akurat menggunakan resolusi 480p atau 720p sebesar 79.51%.

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Image Processing for Rapidly Eye Detection based on Robust Haar Sliding Window

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i2.pp823-830 ◽

2017 ◽

Vol 7 (2) ◽

pp. 823 ◽

Cited By ~ 1

Author(s):

Fitri Utaminingrum ◽

Renaldi Primaswara Praetya ◽

Yuita Arum Sari

Keyword(s):

Image Processing ◽

Eye Movements ◽

Computation Time ◽

Sliding Window ◽

Eye Detection ◽

Improve Accuracy ◽

Speed Up ◽

Facial Area ◽

Window Region ◽

Haar Cascade

Object Detection using Haar Cascade Clasifier widely applied in several devices and applications as a medium of interaction between human and computer such as a tool control that utilizes the detection of eye movements. Obviously speed and precision in the detection process such as eyes, has an effect if implemented on a device. If the eye could not detect accurately, controlling device systems could reach bad detection as well. The proposed method can be used as an approach to detect the eye region of eye based on haar classifier method by means of modifying the direction of sliding window. In which, it was initially placed in the middle position of image on facial area by assuming the location of eyes area in the central region of the image. While the window region of conventional haar cascade scan the whole of image start from the left top corner. From the experiment by using our proposed method, it can speed up the the computation time and improve accuracy significantly reach to 92,4%.

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