scholarly journals PENGENALAN ABJAD SISTEM ISYARAT BAHASA INDONESIA (SIBI) BERBASIS KAMERA DEPTH

LINK ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 6
Author(s):  
Cucun Very Angkoso ◽  
Muhammad Fuad ◽  
Dian Rusdi Hadiwineka
Keyword(s):  
Real Time ◽  
Euclidean Distance ◽  
Microsoft Kinect ◽  
Otsu Thresholding ◽  
Bahasa Indonesia

Interaksi dalam kehidupan sehari-hari umumnya menggunakan bahasa verbal. Penyandang disabilitas tidak dapat menggunakan bahasa verbal, tetapi mereka menggunakan bahasa isyarat yang sulit untuk dimengerti. Sehingga, mereka membutuhkan seorang translator, namun dilain sisi translator tidak dapat memberi mereka privasi. Pada penelitian ini sistem pengenalan isyarat alfabet SIBI dilakukan dengan memanfaatkan kamera depth dari Microsoft kinect. Kinect merupakan sebuah teknologi baru yang dapat memindai gerakan manusia dan suara. Pemanfaatan kinect bertujuan untuk melakukan pengenalan bahasa isyarat secara real-time. Kamera depth menghasilkan citra 3D yang dapat digunakan dalam ruangan gelap dan memungkinkan proses deteksi lebih akurat. Dalam penelitian ini dilakukan segmentasi citra berdasarkan jarak antara obyek dan latar belakang dan hanya menangkap bagian tangan. Binerisasi adalah proses selanjutnya dengan otsu thresholding. Selanjutnya cropping untuk mengambil obyek diperlukan dan resize agar mempermudah dan mempercepat proses selanjutnya. Terakhir, proses pengenalan menggunakan metode euclidean distance berdasarkan nilai jarak terkecil antara template dan obyek. Pengujian template menghasilkan akurasi sebesar 96.538462%. Pengujian real-time menghasilkan akurasi yang baik jika tangan pengguna ditangkap kamera dengan hasil sama seperti template.Kata Kunci: kinect, SIBI, euclidean distance, kamera depth, otsu

scholarly journals Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 286 ◽  
Author(s):  
Ali Al-Naji ◽  
Kim Gibson ◽  
Sang-Heon Lee ◽  
Javaan Chahl
Keyword(s):  
Pilot Study ◽  
Real Time ◽  
Microsoft Kinect ◽  
Kinect Sensor ◽  
Microsoft Kinect Sensor

scholarly journals Real-time virtual mouse system using RGB-D images and fingertip detection

2020 ◽  
Author(s):  
Dinh-Son Tran ◽  
Ngoc-Huynh Ho ◽  
Hyung-Jeong Yang ◽  
Soo-Hyung Kim ◽  
Guee Sang Lee
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Microsoft Kinect ◽  
Desktop Computer ◽  
Light Levels ◽  
Mouse Cursor ◽  
Fingertip Detection ◽  
Rgb Images ◽  
Virtual Mouse ◽  
Hand Region

AbstractA real-time fingertip-gesture-based interface is still challenging for human–computer interactions, due to sensor noise, changing light levels, and the complexity of tracking a fingertip across a variety of subjects. Using fingertip tracking as a virtual mouse is a popular method of interacting with computers without a mouse device. In this work, we propose a novel virtual-mouse method using RGB-D images and fingertip detection. The hand region of interest and the center of the palm are first extracted using in-depth skeleton-joint information images from a Microsoft Kinect Sensor version 2, and then converted into a binary image. Then, the contours of the hands are extracted and described by a border-tracing algorithm. The K-cosine algorithm is used to detect the fingertip location, based on the hand-contour coordinates. Finally, the fingertip location is mapped to RGB images to control the mouse cursor based on a virtual screen. The system tracks fingertips in real-time at 30 FPS on a desktop computer using a single CPU and Kinect V2. The experimental results showed a high accuracy level; the system can work well in real-world environments with a single CPU. This fingertip-gesture-based interface allows humans to easily interact with computers by hand.

Towards a Real-Time Fall Detection System using Kinect Sensor

2016 ◽  
Vol 6 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Nadia Baha ◽  
Eden Beloudah ◽  
Mehdi Ousmer
Keyword(s):  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Microsoft Kinect ◽  
False Alarms ◽  
Depth Information ◽  
Detection Rates ◽  
Major Health Problem ◽  
Major Health ◽  
Time Fall

Falls are the major health problem among older people who live alone in their home. In the past few years, several studies have been proposed to solve the dilemma especially those which exploit video surveillance. In this paper, in order to allow older adult to safely continue living in home environments, the authors propose a method which combines two different configurations of the Microsoft Kinect: The first one is based on the person's depth information and his velocity (Ceiling mounted Kinect). The second one is based on the variation of bounding box parameters and its velocity (Frontal Kinect). Experimental results on real datasets are conducted and a comparative evaluation of the obtained results relative to the state-of-art methods is presented. The results show that the authors' method is able to accurately detect several types of falls in real-time as well as achieving a significant reduction in false alarms and improves detection rates.

Depth-Based Real-Time Gait Recognition

2020 ◽  
Vol 29 (16) ◽  
pp. 2050266
Author(s):  
Adnan Ramakić ◽  
Diego Sušanj ◽  
Kristijan Lenac ◽  
Zlatko Bundalo
Keyword(s):  
Real Time ◽  
Gait Recognition ◽  
Video Stream ◽  
Outdoor Environment ◽  
Microsoft Kinect ◽  
Recognition Method ◽  
Real Time Processing ◽  
Depth Data ◽  
Video Images ◽  
Subject Identification

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

scholarly journals ALGORITMA K-NEAREST NEIGBOR MODEL EUCLIDEAN DISTANCE DALAM KLASIFIKASI KELULUSAN PESERTA BAHASA INDONESIA PENUTUR ASING PADA BALAI BAHASA SUMATERA UTARA

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Hot Riris Siburian ◽  
Efori Buulolo ◽  
Hukendik Hutabarat
Keyword(s):  
Language Learning ◽  
Euclidean Distance ◽  
Language Program ◽  
Learning Skills ◽  
The North ◽  
Object Based ◽  
Distance Model ◽  
Foreign Speakers ◽  
North Sumatra ◽  
Bahasa Indonesia

The North Sumatra Language Center also organizes an Indonesian language program for foreign speakers (BIPA) is an Indonesian language learning skills program (speaking, writing, reading, and listening) for foreign speakers. That so far the Balai Bahasa office has had difficulty classifying the graduation of Indonesian Foreign Speakers (BIPA) participants who are eligible to pass and who have not passed. So that complaints occur from participants.To overcome the above problems, it is necessary to classify graduation based on previous BIPA participant data. K-Nearest Neigbor Euclidean Distance model which is a method for classifying / grouping an object based on certain criteria. By using the K-Nearest neigbor algorithm in grouping BIPA participants by using various criteria, it is hoped that foreigners can speak Indonesian more quickly.Keywords: Indonesian Foreign Speakers, K-Nearest Neigbor Algorithm

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