Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Ankle Active Rehabilitation Strategies Analysis Based on the Characteristics of Human and Robotic Integrated Biomechanics Simulation Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

A Review of Gesture Recognition Based on Computer Vision

verfasst von : Bei Li, Gongfa Li, Ying Sun, Guozhang Jiang, Jianyi Kong, Zhaojie Ju, Du Jiang

Erschienen in: Intelligent Robotics and Applications

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the improvement of computer performance and the development of image processing technology, Gesture recognition based on computer vision has become a hotspot. This paper introduces the main ways of gesture recognition including to data glove, EMG signal and computer vision. The basic principle and working process are focused on computer vision, and describe the technology of gesture segmentation, tracking and positioning, feature extraction and classification recognition, then the main problems existing in recognition method of the computer vision are analyzed. Finally, the future research area of gesture recognition technology in computer vision is prospected.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Rehabilitation Training for Leg Based on EEG-EMG Fusion
Nächstes Kapitel Hand Gesture Recognition Using Interactive Image Segmentation Method
Literatur
1.
Nirmal, K.R., Mishra, N.: 3D graphical user interface on personal computer using P5 data glove. Int. J. Comput. Sci. Issues 8(5), 155–160 (2011)
2.
Camastra, F., Felice, D.D.: LVQ-based hand gesture recognition using a data glove. Neural Nets Surround. 19, 159–168 (2013)CrossRef
3.
Gao, W., Chen, Y.Q., Fang, G.L., Yang, C.S., Jiang, D.L., Ge, C.B., Wang, C.L.: HandTalker II: a Chinese sign language recognition and synthesis system control. In: 8th International Conference on Control, Automation, Robotics and Vision, vol. 1, pp. 759–764 (2004)
4.
Li, D.J., Li, J.X., Zhang, Y.: Gesture recognition of data glove based on PSO-improved BP neural network. Electr. Mach. Control 18(8), 87–93 (2014)
5.
He, J., Zhang, G.F., Dai, S.L.: Simulation system of space teleoperation based on dexterous robot hands. J. Syst. Simul. 21(21), 6915–6919 (2009)
6.
Khezri, M., Jahed, M.: A neuro-fuzzy inference system for sEMG-based identification of hand motion commands. IEEE Trans. Industr. Electron. 58(5), 1952–1960 (2011)CrossRef
7.
Khan, M.S.: sEMG based human computer interface for robotic wheel. In: 2014 International Conference on Advances in Engineering and Technology Research, pp. 1–5 (2014)
8.
Yang, D.P., Zhao, J.D., Li, N.: Recognition of hand grasp preshaping patterns applied to prosthetic hand electromyography control. J. Mech. Eng. 48(15), 1–8 (2012)CrossRef
9.
Fang, Y.F., Liu, H.H., Li, G.F., Zhu, X.Y.: A multichannel surface EMG system for hand motion recognition. Int. J. Humanoid Rob. 12(2), 1–13 (2015)
10.
Liu, N.G., Lei, M.: Characterization of surface electromyography signal based on wavelet analysis and non-linear exponent. J. Clin. Rehabil. Tissue Eng. Res. 12(17), 3285–3288 (2008)
11.
Burck, J., Zeher, M.J., Armiger, R.: Developing the world’s most advanced prosthetic arm using model-based design. In: The MathWorks News and Notes, pp. 1–4 (2009)
12.
Lambrecht, J.M., Pulliam, C.L., Kirsch, R.F.: Virtual reality environment for simulating tasks with a myoelectric prosthesis: an assessment and training tool. J. Prosthet. Orthot. 23(2), 89–94 (2011)CrossRef
13.
Leo, M., Medioni, G., Trivedi, M.: Computer vision for assistive technologies. Comput. Vis. Image Underst. 154, 1–15 (2017)CrossRef
14.
Canal, G., Escalera, S., Angulo, C.: A real-time human-robot interaction system based on gestures for assistive scenarios. Comput. Vis. Image Underst. 149, 65–77 (2016)CrossRef
15.
Tan, Q.S., Yuan, Z.P., Fan, X.C.: Visualized simulation system of a dexterous mechanical gripper. J. Shanghai Univ. 9, 38–42 (2003)
16.
Zhang, G.L., Wang, Z.N., Wang, T.: Survey on dynamic hand gesture recognition with computer vision. J. Huaqiao Univ. 35(6), 653–658 (2014)
17.
Wu, X., Zhang, Q., Xu, Y.X.: An overview of hand gestures recognition. Electron. Sci. Technol. 26(6), 171–174 (2013)
18.
Chen, D.S., Li, G.F., Sun, Y.: An interactive image segmentation method in hand gesture recognition. Sensors 17(2), 1–16 (2017)MathSciNetCrossRef
19.
Chen, D.S., Li, G.F., Jiang, G.Z.: Intelligent computational control of multi-fingered dexterous robotic hand. J. Comput. Theor. Nanosci. 12(12), 6126–6132 (2015)CrossRef
20.
Chen, Q., Georganas, N.D., Petriu, E.M.: Hand gesture recognition using haar-like features and a stoehastic context-free grammar. IEEE Trans. Instrum. Meas. 57(8), 1562–1571 (2008)CrossRef
21.
Piceardi, M.: Background subtraction techniques: a review. IEEE Int. Conf. Syst. Man. Cybern. 4, 3099–3104 (2004)
22.
Raheja, J.L., Chaudhary, A., Sinal, K.: Tracking of fingertips and centres of palm using kinect. In: Proceedings of the 3rd IEEE International Conference on Computational Intelligence, Modelling and Simulation, pp. 248–252 (2011)
23.
Yi, L.: Hand gesture recognition using kinect. In: Proceedings of 2012 IEEE 3rd International Conference on Software Engineering and Service Science, pp. 196–199 (2012)
24.
Cao, C.Q., Li, R.F., Zhao, L.J.: Hand posture recognition method based on depth image technology. Comput. Eng. 38(8), 16–19 (2012)
25.
Vezhnevets, V., Sazonov, V., Andreeva, A.: A survey on pixel-based skin color detection techniques. In: Proceedings of Graphicon, vol. 3, pp. 85–92 (2003)
26.
Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recogn. 40(3), 1106–1122 (2007)CrossRefMATH
27.
Alon, J., Athitsos, V., Yuan, Q., Sclaroff, S.: A unified framework for gesture recognition and spatiotemporal gesture segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1685–1699 (2009)CrossRef
28.
Wachs, J., Stern, H., Edan, Y., Gillam, M., Feied, C., Smith, M., Handler, J.: Gestix: a doctor-computer sterile gesture interface for dynamnic environments. Soft Comput. Ind. Appl. 3, 30–39 (2007)MATH
29.
Peng, J.C., Gu, L.Z., Su, J.B.: The hand tracking for humanoid robot using Camshift algorithm and Kalman filter. J. Shanghai Jiaotong Univ. 40(7), 2161–2165 (2006)
30.
Song, Y., Demirdjian, D., Davis, R.: Multi-signal gesture recognition using temporal smoothing hidden conditional random fields. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1685–1699 (2011)
31.
Shan, C., Tan, T., Wei, Y.: Real-time hand tracking using a mean shift embedded particle filter. Pattern Recogn. 40(7), 1958–1970 (2007)CrossRefMATH
32.
Gianni, F., Collet, C., Dalle, P.: Robust tracking for processing of videos of communication’s gestures. In: Sales Dias, M., Gibet, S., Wanderley, M.M., Bastos, R. (eds.) GW 2007. LNCS, vol. 5085, pp. 93–101. Springer, Heidelberg (2009). doi:10.​1007/​978-3-540-92865-2_​9 CrossRef
33.
Deng, R., Zhou, L.L., Ying, R.D.: Gesture extraction and recognition research based on kinect depth data. Appl. Res. Comput. 30(4), 1263–1265 (2013)
34.
Luo, Y., Xie, Y., Zhang, Y.: Design and implementation of a gesture-driven system for intelligent wheelchairs based on the kinect sensor. Robot 34(1), 110–114 (2012)CrossRef
35.
Dong, L.F., Ruan, J., Ma, Q.S.: The gesture identification based on invariant moments and SVM. Microcomput. Appl. 31(6), 32–35 (2012)
36.
Ren, Y., Zhang, F.M.: Hand gesture recognition based on MEB-SVM. In: International Conferences on Embedded Software and Systems, pp. 344–349 (2009)
37.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2011)
38.
Ren, Y., Gu, C.C.: Hand gesture recognition based on HOG characters and SVM. Bull. Sci. Technol. 27(2), 211–213 (2011)
39.
Ahmed, B.J., Walid, M., Yousra, B.J., Abdelmajid, B.H.: A new approach for digit recognition based on hand gesture analysis. Int. J. Comput. Sci. Inf. Secur. 2(1), 1–8 (2009)
40.
Weng, H.L., Zhan, Y.W.: Vision-based hand gesture recognition with multiple cues. Comput. Eng. Sci. 34(2), 123–127 (2012)
41.
Hu, R.X., Jia, W., Zhang, D., Gui, J., Song, L.T.: Hand shape recognition based on coherent distance shape contexts. Pattern Recogn. 45(9), 3348–3359 (2012)CrossRef
42.
Belsare, S., Sujatha, K.: Robust part-based hand gesture recognition using kinect sensor. Int. J. Eng. Sci. Res. Technol. 4(8), 75–80 (2015)
43.
Hasan, H., Kareem, S.A.: Static hand gesture recognition using neural networks. Artif. Intell. Rev. 41(2), 147–181 (2014)CrossRef
44.
Yang, Z.Q., Sun, G.: Gesture recognition based on quantum-behaved particle swarm optimization of back propagation neural network. J. Comput. Appl. 34(S1), 137–140 (2014)
45.
Kim, D., Song, J.Y., Kim, D.: Simultaneous gesture segmentation and recognition based on forward spotting accumulative HMMs. Pattern Recogn. 40(11), 3012–3026 (2007)CrossRefMATH
46.
Zheng, W., Shen, X.K.: Algorithm based on continuous data stream for dynamic gesture recognition. J. Beijing Univ. Aeronaut. Astronaut. 38(2), 274–280 (2012)
47.
Abdelkader, M.F., Abd-Almageed, W., Srivastava, A., Chellappa, R.: Silhouette-based gesture and action recognition via modeling trajectories on Riemannian shape manifolds. Comput. Vis. Image Underst. 115(3), 439–455 (2011)CrossRef
Metadaten
Titel
A Review of Gesture Recognition Based on Computer Vision
verfasst von
Bei Li
Gongfa Li
Ying Sun
Guozhang Jiang
Jianyi Kong
Zhaojie Ju
Du Jiang
Copyright-Jahr
2017
Buch
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65288-7

Electronic ISBN: 978-3-319-65289-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-65289-4_50