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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Global-Local Approach to Extracting Deformable Fashion Items from Web Images Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Semi-supervised Learning for Human Pose Recognition with RGB-D Light-Model

verfasst von : Xinbo Wang, Guoshan Zhang, Dahai Yu, Dan Liu

Erschienen in: Advances in Multimedia Information Processing - PCM 2016

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

This work targets human pose recognition based on RGB-D videos. In recently, RGB-D based methods can be typically represented as either maps-based approaches or skeleton-based approaches. This paper proposes a semi-supervised learning method for evaluating human posture via RGB-D and light-model. The light-model is generated to represent depth sequence, by using the dynamic-fusion strategy. In this regard, light-model has richer information than depth image, and a CNN classifier is further constructed to recognize human pose with trained labeled light model data. Soft correlation and hard correlation are used to adjust the CNN output of non-labeled data. This paper constructs a set of posture data which consist of RGB images and light model. The experiments results show that our method is more accuracy than the state of the art, and the efficient is also competitive. This study implies that feature extracted from 3D models is reliable for human pose recognition, especially for sitting posture.

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 Scene Parsing with Deep Features and Spatial Structure Learning
Literatur
1.
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: 17th International Conference on Proceedings of the Pattern Recognition (ICPR 2004), vol. 3, pp. 32–36. IEEE Computer Society (2004)
2.
Heng, W., Schmid, C.: Action recognition with improved trajectories. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE (2013)
3.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
4.
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM Multimedia, vol. 2 (2014)
5.
Chéron, G., Laptev, I., Schmid, C.: P-CNN: pose-based CNN features for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
6.
Tran, D., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
7.
Karpathy, A., et al.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2014)
8.
Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)CrossRef
9.
Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE (2013)
10.
Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
11.
Lu, X., Aggarwal, J.K.: Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)
12.
Oreifej, O., Liu, Z.: HON4D: histogram of oriented 4d normals for activity recognition from depth sequences. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)
13.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems (2014)
14.
15.
Wang, K., Wang, X., Lin, L., et al.: 3D human activity recognition with reconfigurable convolutional neural networks. In: Proceedings of the ACM International Conference on Multimedia. ACM (2014)
16.
Whelan, T., et al.: Kintinuous: spatially extended kinectfusion. MIT-CSAIL-TR-2012-020 (2012)
17.
Nießner, M., et al.: Real-time 3d reconstruction at scale using voxel hashing. ACM Trans. Graph. (TOG) 32(6) (2013). Article No. 169
18.
Whelan, T., et al.: ElasticFusion: dense SLAM without a pose graph. In: RSS (2015)
19.
Blan, A.O., et al.: Shining a light on human pose: on shadows, shading and the estimation of pose and shape. In: IEEE 11th International Conference on Computer Vision, ICCV 2007. IEEE (2007)
20.
Lee, M.W., Nevatia, R.: Body part detection for human pose estimation and tracking. In: IEEE Workshop on Motion and Video Computing, WMVC 2007. IEEE (2007)
21.
Lee, M.W., Nevatia, R.: Dynamic human pose estimation using Markov chain Monte Carlo approach. In: Seventh IEEE Workshops on Application of Computer Vision, WACV/MOTIONS 2005, vol. 1–2. IEEE (2005)
22.
Fathi, A., Mori, G.: Human pose estimation using motion exemplars. In: IEEE 11th International Conference on Computer Vision, ICCV 2007. IEEE (2007)
23.
Baumberg, A.M., Hogg, D.C.: An efficient method for contour tracking using active shape models. In: Proceedings of the 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects. IEEE (1994)
24.
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2010)
25.
Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z., Campos, M.F.M.: STOP: Space-Time Occupancy Patterns for 3D action recognition from depth map sequences. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 252–259. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-33275-3_​31 CrossRef
26.
Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3d action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7441, pp. 872–885. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-33709-3_​62
27.
Mao, Y., et al.: Accurate 3d pose estimation from a single depth image. In: 2011 IEEE International Conference on Computer Vision (ICCV). IEEE (2011)
28.
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MCV 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-18421-5_​11 CrossRef
29.
Jalal, A., et al.: Recognition of human home activities via depth silhouettes and transformation for smart homes. Indoor Built Environ. 21(1), 184–190 (2011)CrossRef
30.
Yang, X., Zhang, C., Tian, Y.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM International Conference on Multimedia. ACM (2012)
31.
Wu, S.-L., Cui, R.-Y.: Human behavior recognition based on sitting postures. In: 2010 International Symposium on Computer Communication Control and Automation (3CA), vol. 1. IEEE (2010)
32.
33.
34.
Stikic, M., Van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 12th IEEE International Symposium on Wearable Computers, ISWC 2008. IEEE (2008)
35.
Zhao, X., et al.: Human action recognition based on semi-supervised discriminant analysis with global constraint. Neurocomputing 105, 45–50 (2013)CrossRef
36.
Zhang, T., et al.: Boosted multi-class semi-supervised learning for human action recognition. Pattern Recogn. 44(10), 2334–2342 (2011)CrossRefMATH
37.
Guan, D., et al.: Activity recognition based on semi-supervised learning. In: 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2007. IEEE (2007)
38.
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39, 1–38 (1977)
39.
Miller, D.J., Uyar, H.S.: A mixture of experts classifier with learning based on both labelled and unlabelled data. In: Advances in Neural Information Processing Systems (1997)
40.
Zhao, Y., et al.: Combing RGB and depth map features for human activity recognition. In: 2012 Asia-Pacific on Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE (2012)
41.
Faria, D.R., Premebida, C., Nunes, U.: A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In: 2014 RO-MAN: The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE (2014)
42.
Ming, Y., Ruan, Q., Hauptmann, A.G.: Activity recognition from RGB-D camera with 3d local spatio-temporal features. In: 2012 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2012)
Metadaten
Titel
Semi-supervised Learning for Human Pose Recognition with RGB-D Light-Model
verfasst von
Xinbo Wang
Guoshan Zhang
Dahai Yu
Dan Liu
Copyright-Jahr
2016
Buch
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48895-0

Electronic ISBN: 978-3-319-48896-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48896-7_72

Neuer Inhalt

    Bildnachweise