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:
Blind Motion Deblurring Based on Fused - Regularization Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Human-Object Interaction Recognition by Modeling Context

verfasst von : Qun Zhang, Wei Liang, Xiabing Liu, Yumeng Wang

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In this paper, we present a new method to recognize human-object interactions by modeling the context between human actions and manipulated objects. It is a challenging task due to severe occlusion between human and objects during the interacting process. While human actions and objects can provide strong context information, such as some action happening is usually related to a certain object, by which we can improve the accuracy of recognition for both of them. In this paper, we use global and local temporal features from skeleton sequences to model actions, and kernel features are applied to describe objects. We optimize all possible solutions from actions and objects by modeling the context between them. The results of experiments show the effectiveness of our method.

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 Hierarchical Convolutional Neural Network for Face Detection
Nächstes Kapitel Image Annotation Based on Multi-view Learning
Literatur
1.
Bo, L., Ren, X., Fox, D.: Kernel descriptors for visual recognition. In: Advances in Neural Information Processing Systems, pp. 244–252 (2010)
2.
Bo, L., Ren, X., Fox, D.: Depth kernel descriptors for object recognition. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 821–826. IEEE (2011)
3.
Bo, L., Sminchisescu, C.: Efficient match kernel between sets of features for visual recognition. In: Advances in Neural Information Processing Systems, pp. 135–143 (2009)
4.
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)CrossRef
5.
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
6.
Chen, D.Y., Shih, S.W., Liao, H.Y.: Human action recognition using 2-d spatio-temporal templates. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 667–670. IEEE (2007)
7.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
8.
Flitton, G.T., Breckon, T.P., Bouallagu, N.M.: Object recognition using 3d sift in complex CT volumes. In: BMVC, pp. 1–12 (2010)
9.
Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: 2005 Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1458–1465. IEEE (2005)
10.
Gupta, A., Kembhavi, A., Davis, L.S.: Observing human-object interactions: using spatial and functional compatibility for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(10), 1775–1789 (2009)CrossRef
11.
Ito, S., Kubota, S.: Object classification using heterogeneous co-occurrence features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 701–714. Springer, Heidelberg (2010) CrossRef
12.
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 951–958. IEEE (2009)
13.
Liang, Y., Lu, W., Liang, W., Wang, Y.: Action recognition using local joints structure and histograms of 3d joints. In: 2014 Tenth International Conference on Computational Intelligence and Security (CIS), pp. 185–188. IEEE (2014)
14.
Lin, Z., Jiang, Z., Davis, L.S.: Recognizing actions by shape-motion prototype trees. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 444–451. IEEE (2009)
15.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef
16.
Marszalek, M., Laptev, I., Schmid, C.: Actions in context. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2929–2936. IEEE (2009)
17.
Raptis, M., Soatto, S.: Tracklet descriptors for action modeling and video analysis. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 577–590. Springer, Heidelberg (2010) CrossRef
18.
Su, Y., Allan, M., Jurie, F.: Improving object classification using semantic attributes. In: BMVC, pp. 1–10 (2010)
19.
Sun, J., Wu, X., Yan, S., Cheong, L.F., Chua, T.S., Li, J.: Hierarchical spatio-temporal context modeling for action recognition. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2004–2011. IEEE (2009)
20.
Wei, P., Zhao, Y., Zheng, N., Zhu, S.C.: Modeling 4d human-object interactions for event and object recognition. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3272–3279. IEEE (2013)
21.
Xia, L., Chen, C.C., Aggarwal, J.: View invariant human action recognition using histograms of 3d joints. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20–27. IEEE (2012)
22.
Yao, B., Fei-Fei, L.: Modeling mutual context of object and human pose in human-object interaction activities. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17–24. IEEE (2010)
23.
Yao, B., Fei-Fei, L.: Recognizing human-object interactions in still images by modeling the mutual context of objects and human poses. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1691–1703 (2012)CrossRef
24.
Yu, K., Xu, W., Gong, Y.: Deep learning with kernel regularization for visual recognition. In: Advances in Neural Information Processing Systems, pp. 1889–1896 (2009)
25.
Zhang, J., Huang, K., Yu, Y., Tan, T.: Boosted local structured HOG-LBP for object localization. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1393–1400. IEEE (2011)
Metadaten
Titel
Human-Object Interaction Recognition by Modeling Context
verfasst von
Qun Zhang
Wei Liang
Xiabing Liu
Yumeng Wang
Copyright-Jahr
2015
Buch
Image and Graphics

Print ISBN: 978-3-319-21962-2

Electronic ISBN: 978-3-319-21963-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-21963-9_35

Premium Partner

    Bildnachweise