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

ANIL BAS, (Marmara Üniversitesi, Bilgisayar Mühendisliği Bölümü, İstanbul, Türkiye)
HASAN ERDİNÇ KOÇER (Selçuk Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Bölümü, Konya, Türkiye)
Balkan Journal of Electrical and Computer Engineering
2 1
Yıl: 2021 Cilt: 9 Sayı: 3 Sayfa Aralığı: 229 - 234 Metin Dili: İngilizce DOI: 10.17694/bajece.845276 İndeks Tarihi: 21-12-2021

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

Öz:
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.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [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”, Human Movement Science, Vol. 32, No. 3, 2013, pp. 511–526.
  • [15] X. Wang, K. Qin, “A Six-degree-of-freedom Virtual Mouse based on Hand Gestures”, International Conference on Electrical and Control Engineering, Wuhan, China, 25–27 June 2010.
  • [16] N.H. Dardas, N.D. Georganas, “Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques”, IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 11, 2011, pp. 3592–3607.
  • [17] N.H. Dardas, E.M. Petriu, “Hand gesture detection and recognition using principal component analysis”, IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Ottawa, Canada, 19–21 September 2011.
  • [18] C.C. Hsieh, D.H. Liou, D. Lee, “A real time hand gesture recognition system using motion history image”, 2nd International Conference on Signal Processing Systems, Dalian, China, 5–7 July 2010.
  • [19] P. Trigueiros, F. Ribeiro, L.P. Reis, “A comparison of machine learning algorithms applied to hand gesture recognition”, 7th Iberian Conference on Information Systems and Technologies, Madrid, Spain, 20–23 June 2012.
  • [20] E. Sangineto, M. Cupelli, “Real-time viewpoint-invariant hand localization with cluttered backgrounds”, Image and Vision Computing, Vol. 30, 2012, pp. 26–37.
  • [21] B. Toni, J. Darko, “A robust hand detection and tracking algorithm with application to natural user interface”, 35th International Convention MIPRO, Opatija, Croatia, 21–25 May 2012.
  • [22] K. Jacobs, M. Ghasiazgar, I. Venter, R. Dodds, “Hand Gesture Recognition of Hand Shapes in Varied Orientations using Deep Learning”, Annual Conference of the South African Institute of Computer Scientists and Information Technologists, Johannesburg, South Africa, 26–28 September 2016.
  • [23] P. Molchanov, S. Gupta, K. Kim, J. Kautz, “Hand gesture recognition with 3D convolutional neural networks”, IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, United States, 7–12 June 2015.
  • [24] Microsoft Kinect, “Kinect for Windows”, https://developer.microsoft.com/windows/kinect (24.12.2020).
  • [25] Ultraleap, “Leap Motion Controller”, http://www.ultraleap.com/product/leap-motion-controller (24.12.2020).
  • [26] C. Kanan, G.W. Cottrell, “Color-to-grayscale: does the method matter in image recognition?”, PloS one, Vol. 7, 2012, pp. e29740:1–7.
  • [27] K. Toyama, J. Krumm, B. Brumitt, B. Meyers, “Wallflower: Principles and practice of background maintenance”, International Conference on Computer Vision, Kerkyra, Greece, 20–27 September 1999.
  • [28] P. Viola, M. Jones, “Rapid object detection using a boosted cascade of simple features”, IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, United States, 8–14 December 2001.
  • [29] S.A. Dabhade, M.S. Bewoor, “Real time face detection and recognition using haar-based cascade classifier and principal component analysis”, International Journal of Computer Science and Management Research, 2012, pp. 59–64.
  • [30] R. Padilla, C. Costa Filho, M. Costa, “Evaluation of haar cascade classifiers designed for face detection”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 6, No. 4, 2012, pp. 466–469.
  • [31] S. Mattoccia, F. Tombari, L. Di Stefano, “Efficient template matching for multi-channel images”, Pattern Recognition Letters, Vol. 32, No. 5, 2011, pp. 694–700.
  • [32] Emgu CV, “Emgu CV: OpenCV in .NET”, http://www.emgu.com (24.12.2020).
  • [33] C.O. Conaire, N.E. O’Connor, A.F. Smeaton, “Detector adaptation by maximising agreement between independent data sources”, IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, United States, 17–22 June 2007.
APA Bas A, Kocer H (2021). Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. , 229 - 234. 10.17694/bajece.845276
Chicago Bas Anil,Kocer Hasan Erdinc Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. (2021): 229 - 234. 10.17694/bajece.845276
MLA Bas Anil,Kocer Hasan Erdinc Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. , 2021, ss.229 - 234. 10.17694/bajece.845276
AMA Bas A,Kocer H Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. . 2021; 229 - 234. 10.17694/bajece.845276
Vancouver Bas A,Kocer H Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. . 2021; 229 - 234. 10.17694/bajece.845276
IEEE Bas A,Kocer H "Real-Time Hand Motion Recognition: A Robust Low-Cost Approach." , ss.229 - 234, 2021. 10.17694/bajece.845276
ISNAD Bas, Anil - Kocer, Hasan Erdinc. "Real-Time Hand Motion Recognition: A Robust Low-Cost Approach". (2021), 229-234. https://doi.org/10.17694/bajece.845276
APA Bas A, Kocer H (2021). Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. Balkan Journal of Electrical and Computer Engineering, 9(3), 229 - 234. 10.17694/bajece.845276
Chicago Bas Anil,Kocer Hasan Erdinc Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. Balkan Journal of Electrical and Computer Engineering 9, no.3 (2021): 229 - 234. 10.17694/bajece.845276
MLA Bas Anil,Kocer Hasan Erdinc Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. Balkan Journal of Electrical and Computer Engineering, vol.9, no.3, 2021, ss.229 - 234. 10.17694/bajece.845276
AMA Bas A,Kocer H Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. Balkan Journal of Electrical and Computer Engineering. 2021; 9(3): 229 - 234. 10.17694/bajece.845276
Vancouver Bas A,Kocer H Real-Time Hand Motion Recognition: A Robust Low-Cost Approach. Balkan Journal of Electrical and Computer Engineering. 2021; 9(3): 229 - 234. 10.17694/bajece.845276
IEEE Bas A,Kocer H "Real-Time Hand Motion Recognition: A Robust Low-Cost Approach." Balkan Journal of Electrical and Computer Engineering, 9, ss.229 - 234, 2021. 10.17694/bajece.845276
ISNAD Bas, Anil - Kocer, Hasan Erdinc. "Real-Time Hand Motion Recognition: A Robust Low-Cost Approach". Balkan Journal of Electrical and Computer Engineering 9/3 (2021), 229-234. https://doi.org/10.17694/bajece.845276
Sayfa Oluşturulma Tarihi
23-04-2024 20:44

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları