Real-Time Detection and Clasification System of Biosecurity Elements Using Haar Cascade Classifier with Open Source

2020 ◽  
Author(s):  
Carlos Vicente Nino Rondon ◽  
Sergio Alexander Castro Casadiego ◽  
Byron Medina Delgado ◽  
Dinael Guevara Ibarra ◽  
Miguel Eduardo Posada Haddad
Keyword(s):  
Open Source ◽  
Real Time ◽  
Cascade Classifier ◽  
Haar Cascade ◽  
Real Time Detection

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

CYCLOTRON ◽  
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

scholarly journals Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

2021 ◽  
Vol 11 (1) ◽  
pp. 45-58
Author(s):  
R. Rizal Isnanto ◽  
Adian Rochim ◽  
Dania Eridani ◽  
Guntur Cahyono
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Video Data ◽  
Cascade Classifier ◽  
Low Resolution ◽  
The Face ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Low Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

scholarly journals Sistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier

Techno Com ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 97-109
Author(s):  
Muhammad Zulfikri ◽  
Erni Yudhaningtyas ◽  
Rahmadwati Rahmadwati
Keyword(s):  
Real Time ◽  
Cascade Classifier ◽  
Integral Image ◽  
Haar Features ◽  
Haar Cascade

Kecelakaan lalu lintas sering terjadi disebabkan kendaraan yang melaju dengan kecepatan tinggi. Penelitian ini mengembangkan sistem penegakan speed bump (polisi tidur) yang dapat memberikan peringatan bagi pengemudi dalam memperlambat laju kendaraan dan memberikan kenyamanan saat melaju dengan kecepatan rendah. Penegakan speed bump dilakukan berdasarkan kecepatan kendaraan yang terdeteksi menggunakan metode Haar Cascade Classifier, yang merupakan gabungan beberapa konsep yaitu Haar Features, Integral Image, AdaBoost Learning, dan Cascade Classifier. Pengujian dilakukan menggunakan video jalan raya pada satu jalur. Sistem dibuat menggunakan interpreter python dengan library OpenCV. Pendeteksian didapatkan hasil yang cukup baik apabila dilakukan pada intensitas cahaya tinggi, dan didapatkan tingkat akurasi deteksi sebesar 97,92%. Perhitungan kecepatan kendaraan didapatkan dengan membandingkan hasil kecepatan pada sistem dengan video dalam keadaan real time, yang dibuktikan dari tingkat error dengan nilai MSE yaitu 2,88.

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>

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection
Sign in / Sign up

Export Citation Format

Share Document