nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Hand Gesture Recognition by Using 3DCNN and LSTM with Adam Optimizer

verfasst von : Siyu Jiang, Yimin Chen

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

A two-step hand gesture recognition system is proposed to classify gestures from different subjects performed under widely varying lighting conditions. First, 3D Convolutional neural network are fine-tuned to classify each hand gesture. Then, the fine-tuned 3D Convolutional neural network are used to learn spatio-temporal features for Long short-term memory automatically. We also perform spatiotemporal data augmentation for more effective training to reduce potential overfitting. In addition, Adam optimizer is employed to improve training speed in both steps. On the VIVA challenge dataset, our method achieves a correct classification rate of \(94.5\%\), and experimental result shows that Adam optimizer outperforms the most commonly used optimizer SGD. Moreover, our system has strong robustness in different lighting conditions.

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 Visual Tracking by Deep Discriminative Map

Nächstes Kapitel Learning Temporal Context for Correlation Tracking with Scale Estimation

Starner, T., Weaver, J., Pentland, A.: Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1371–1375 (1998)CrossRef

Wang, S.B., Quattoni, A., Morency, L.P., et al.: Hidden conditional random fields for gesture recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1521–1527. IEEE (2006)

Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Meas. 60(11), 3592–3607 (2011)CrossRef

Ohn-Bar, E., Trivedi, M.M.: Hand gesture recognition in real time for automotive interfaces: a multimodal vision-based approach and evaluations. IEEE Trans. Intell. Transp. Syst. 15(6), 2368–2377 (2014)CrossRef

Elmezain, M., Al-Hamadi, A., Appenrodt, J., et al.: A hidden markov model-based continuous gesture recognition system for hand motion trajectory. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)

Neverova, N., Wolf, C., Taylor, G.W., Nebout, F.: Multi-scale deep learning for gesture detection and localization. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8925, pp. 474–490. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16178-5_33CrossRef

Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25446-8_4CrossRef

Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)MathSciNetMATH

Tran, D., Bourdev, L., Fergus, R., et al.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)

10.

Ji, S., Xu, W., Yang, M., et al.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)CrossRef

11.

Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J.: Online detection and classification of dynamic hand gestures with recurrent 3D convolutional neural networks

12.

Molchanov, P., Gupta, S., Kim, K., et al.: Hand gesture recognition with 3D convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–7 (2015)

13.

Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

14.

Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

15.

Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH

16.

Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw. Mach. Learn. 4(2), 26–31 (2012)

17.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRef

18.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

19.

Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

20.

Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 677–695 (1997)CrossRef

21.

Trindade, P., Lobo, J., Barreto, J.P.: Hand gesture recognition using color and depth images enhanced with hand angular pose data. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 71–76. IEEE (2012)

22.

Zobl, M., Nieschulz, R., Geiger, M., Lang, M., Rigoll, G.: Gesture components for natural interaction with in-car devices. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 448–459. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24598-8_41CrossRef

23.

Althoff, F., Lindl, R., Walchshausl, L., et al.: Robust multimodal hand-and head gesture recognition for controlling automotive infotainment systems. VDI BERICHTE 1919, 187 (2005)

24.

Parada-Loira, F., Gonzlez-Agulla, E., Alba-Castro, J.L.: Hand gestures to control infotainment equipment in cars. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 1–6. IEEE (2014)

25.

Karpathy, A., Toderici, G., Shetty, S., et al.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)

26.

Molchanov, P., Gupta, S., Kim, K., et al.: Multi-sensor system for driver’s hand-gesture recognition. In: Automatic Face and Gesture

27.

Hoskisson, R.E., Hitt, M.A., Johnson, R.A., et al.: Construct validity of an objective (entropy) categorical measure of diversification strategy. Strateg. Manag. J. 14(3), 215–235 (1993)CrossRef

Titel: Hand Gesture Recognition by Using 3DCNN and LSTM with Adam Optimizer
verfasst von: Siyu Jiang
Yimin Chen
Verlag: Springer International Publishing
Buch: Advances in Multimedia Information Processing – PCM 2017
Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-77380-3_71

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Nachhaltigkeitsaward Key Visual/© Cometis AG/Global ESG Monitor | Daniel Rupp | Generiert mit KI, Search Icon, Banner Hanser, Jonas Klose/© Pine Valley Capital GmbH, Carina Kießling von der Strategieberatung Roland Berger/© Monika Walther Fotografie | ATZ, Beijing Auto Show 2024: Deutsche Hersteller wollen angreifen./© EKH-Pictures / Generated with AI / Stock.adobe.com, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, Zukunftswerkstatt Sales Excellence 2024/© AndreyPopov / Getty Images / iStock, 2023_Antrieb/© supervisuell, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Neuer Inhalt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.