Hand Motion Gesture for Human-Computer Interaction Using Support Vector Machine and Hidden Markov Model
| Main Author: | Suharjito, Suharjito |
|---|---|
| Format: | Article PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
Praise Worthy Prize
, 2016
|
| Subjects: | |
| Online Access: |
http://eprints.binus.ac.id/33594/1/Optimization%20of%20Land%20Suitability%20for%20Food%20Crops%20using%20Neural%20Network%20and%20Swa.pdf http://eprints.binus.ac.id/33594/ http://www.praiseworthyprize.org/jsm/index.php?journal=irecos&page=article&op=view&path%5B%5D=18664 |
Daftar Isi:
- Hand gesture recognition for human-computer interaction has become very popular in recent times. The main problem of this technology is how the system can recognize the presence of a gesture in a streaming video. In this paper, we propose a model that can recognize hand motion gesture in avideo stream using Support Vector Machine and Hidden-Markov model. Support Vector Machine has the advantage of generalizing classification. On the other hand, Hidden Markov Model is a statistical model that is capable of modeling Spatial-temporal time series. This system is divided into two main processes. First, this system recognizes hand posture using SMV (static gesture recognition) and generates sequence observation which is used for the second process later. The second process is recognizing dynamic hand gesture with the sequence observation from the static gesture. The implementation shows that static hand gesture recognition achieves average accuracy at 91% using testing dataset. Meanwhile,dynamically isolated hand gesture recognition gets average accuracy at 89% using testing dataset. We also have tested the system with continuous dynamic gesture using video stream. The system can recognize the gesture very well with accuracy of 83%. This achievement shows that the model can be used in human computer-interaction with specific supports such as Vector Machine and Hidden Markov Model parameter.