KaydedilenlerDerginizi ekleyin

Dergiler / Balkan Journal of Electrical and Computer Engineering / 2021 / Cilt: 9 - Sayı: 3

Real-Time Hand Motion Recognition: A Robust Low-Cost Approach

HASAN ERDİNÇ KOÇER
ANIL BAS
Sayfa
229–234
Yayın Tarihi
2022-10-11
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The method has a wide range of applications where more natural ways of interaction required, such as virtual reality applications, assistive technologies and simulation.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This study presents a robust, low-cost hand motion recognition approach designed to run on low-end computer systems.
📊
Temel Bulgular & Veri:The experimental results show promising performance under various conditions.
💡
Sonuç & Katkı:The method has a wide range of applications where more natural ways of interaction required, such as virtual reality applications, assistive technologies and simulation.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

This study presents a robust, low-cost hand motion recognition approach designed to run on low-end computer systems. Our method detects and tracks hand region using realtime images obtained from a low-resolution camera (i.e. webcam) and is not depended on any training or calibration and is not required any special camera apparatus or selectors. The proposed system involves several image processing techniques such as background subtraction, face detection, skin colour detection and template matching. The experimental results show promising performance under various conditions. The method has a wide range of applications where more natural ways of interaction required, such as virtual reality applications, assistive technologies and simulation.

🔖

Bu Makaleye Atıf Yap

KOÇER, H. E., BAS, A. (2022). Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. Balkan Journal of Electrical and Computer Engineering, Cilt: 9 - Sayı: 3, 229–234. https://ajindex.com/articles/460437

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. Z. Merchant, E.T. Goetz, L. Cifuentes, W. Keeney-Kennicutt, T.J. Davis, “Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis”, Computers & Education, Vol. 70, 2014, pp. 29–40
  2. D. Chambers, “‘Wii play as a family’: the rise in family-centred video gaming”, Leisure Studies, Vol. 31, 2012, pp. 69–82
  3. H.H. Mousavi, M. Khademi, “A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation”, Journal of Medical Engineering, 2014
  4. N.E. Seymour, A.G. Gallagher, S.A. Roman, M.K. O’brien, V.K. Bansal, D.K. Andersen, R.M. Satava, “Virtual reality training improves operating room performance: results of a randomized, double-blinded study”, Annals of Surgery, Vol. 236, No. 4, 2002, pp. 458–464
  5. P.W. Lee, H.Y. Wang, Y.C. Tung, J.W. Lin, A. Valstar, “TranSection: hand-based interaction for playing a game within a virtual reality game”, 33rd ACM Conference on Human Factors in Computing Systems, Seoul, South Korea, 18 – 23 April 2015
  6. O. Aran, Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components, PhD Thesis, Bogazici University, Istanbul, Turkey, 2008
  7. Y. Yin, Toward an intelligent multimodal interface for natural interaction. MSc Thesis, Massachusetts Institute of Technology, Cambridge, MA, United States, 2010
  8. X. Yang, A hand input-based approach to intuitive human-computer interactions in virtual reality. MPhil Thesis, The University of Hong Kong, Pokfulam, Hong Kong, 2010
  9. N. Ikizler, Understanding human motion: recognition and retrieval of human activities, PhD Thesis, Bilkent University; Ankara, Turkey, 2008
  10. M. Al-Rajab, Hand gesture recognition for multimedia applications, PhD Thesis, University of Leeds, Leeds, UK, 2008
  11. C.P. Chen, Y.T. Chen, P.H. Lee, Y.P. Tsai, S. Lei, “Real-time hand tracking on depth images”, Visual Communications and Image Processing, 2011, pp. 1–4
  12. C. Keskin, L. Akarun, “STARS: Sign tracking and recognition system using input–output HMMs”, Pattern Recognition Letters, Vol. 30, No. 12, 2009, pp. 1086–1095
  13. L. Lamberti, F. Camastra, “Handy: a real-time three color glove-based gesture recognizer with learning vector quantization”, Expert Systems with Applications, Vol. 39, No. 12, 2012, pp. 10489–10494
  14. Y.H. Lee, S.K. Wu, Y.P. Liu, “Performance of remote target pointing hand movements in a 3D environment”, H