scholarly journals PEMBUATAN HAAR-CASCADE DAN LOCAL BINARY PATTERN SEBAGAI SISTEM PENDETEKSI HALANGAN PADA AUTOMATIC GUIDED VEHICLE

2018 ◽  
Vol 9 (2) ◽  
pp. 1073-1082
Author(s):  
Riza Agung Firmansyah ◽  
Enggar Alfianto
Keyword(s):  
Local Binary Pattern ◽  
Cascade Classifier ◽  
Automatic Guided Vehicle ◽  
Haar Cascade

Automatic Guided Vehicle (AGV) merupakan salah satu jenis robot yang bekerja mengikuti suatu jalur. Dalam penelitian ini, AGV digunakan di lingkungan perkantoran kampus. Lingkungan kampus menyebabkan jalur yang dilewati sulit dikondisikan dalam kondisi steril karena banyak objek penghalang. Hal ini membuat robot harus memiliki sistem pendeteksi halangan yang mampu membedakan halangan diam dan bergerak. Dalam penelitian ini penghalang diam berupa bak sampah dan penghalang bergerak adalah manusia. Untuk mendeteksi penghalang tersebut digunakan haar-cascade sebagai pencarian kasar dan local binary pattern (LBP) sebagai pencarian halus. Haar-cascade dibuat dengan memanfaatkan opencv haar training. Training dilakukan dengan menggunakan 300 citra positif dan 2317 citra negative pada masing-masing objek. Haar-cascade classifier didapatkan setelah dilakukan training hingga 10 stage. Haar-cascade diuji pada jarak dibawah 4 meter dari objek. Pencarian halus menggunakan LBP dilakukan saat haar-cascade mendeteksi adanya objek lebih dari satu. Dari pengujian yang telah dilakukan, sistem berhasil mendeteksi adanya halangan dengan tingkat keberhasilan 81,7%.

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 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

Arabic character recognition using a Haar cascade classifier approach (HCC)

2015 ◽  
Vol 19 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Ashraf AbdelRaouf ◽  
Colin A. Higgins ◽  
Tony Pridmore ◽  
Mahmoud I. Khalil
Keyword(s):  
Character Recognition ◽  
Cascade Classifier ◽  
Haar Cascade
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