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:
Cognitive Radio Interference Modeling and Application on Fading Channels Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

59. Semantic Analysis in Human Action Recognition: A Comprehensive Study

verfasst von : Zhong Zhang, Shuang Liu, Shuaiqi Liu, Liang Han, Yunxue Shao

Erschienen in: The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Human action recognition is a hot topic in computer vision and pattern recognition. Semantic analysis, as a kind of effective bridge to connect the human brains and computers, has been widely studied over the past several years and a number of methods have been proposed. However, there is no comprehensive study concerning semantic analysis in human action recognition. In this paper, we make a survey on various semantic analysis methods based on an approach-based taxonomy. We choose several representatives from semantic analysis methods and evaluate them on publicly available datasets.

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 Human Action Recognition using Salient Region Detection in Complex Scenes
Nächstes Kapitel DOA Estimation of Coherent and Incoherent Wideband OFDM Signals Based on Fourth-Order Cumulants
Literatur
1.
Schuldt C, Laptev I, Caputo, B (2004) Recognizing human actions: a local SVM approach. In: International conference on pattern recognition, pp 32–36
2.
Sullivan M, Shah M (2008) Action MACH: Maximum average correlation height filter for action recognition. In: IEEE conference on computer vision and pattern recognition
3.
Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos in the wild. In: IEEE conference on computer vision and pattern recognition, pp 1996–2003
4.
Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv (CSUR) 43(3):1–43CrossRef
5.
Wang H, Ullah M, Klaser A, Laptev I, Schmid C (2009) Evaluation of local spatio-temporal features for action recognition. In: British machine vision conferenceCrossRef
6.
Csurka G, Dance C, Fan L (2004) Visual categorization with bags of keypoints. In: ECCV workshop on statistical learning in computer vision, pp 1–22
7.
Savarese S, DelPozo A, Niebles J, Fei-Fei L (2008) Spatial-temporal correlatons for unsupervised action classification. In: IEEE workshop on motion and video computing, pp 1–8
8.
Ryoo M, Aggarwal J (2009) Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: IEEE international conference on computer vision, pp 1593–1600
9.
Kovashka A, Grauman K (2010) Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In: IEEE conference on computer vision and pattern recognition, pp 2046–2053
10.
Wang J, Chen Z, Wu Y (2011) Action recognition with multiscale spatio-temporal contexts. In: IEEE conference on computer vision and pattern recognition, pp 3185–3192
11.
Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2012) Action recognition using context-constrained linear coding. IEEE Signal Process Lett 19(7):439–442CrossRef
12.
Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2012) Contextual Fisher kernels for human action recognition. In: International conference on pattern recognition, pp 437–440
13.
Yilmaz A, Shah M (2005) Actions sketch: A novel action representation. In: IEEE conference on computer vision and pattern recognition, pp 984–989
14.
Parameswaran V, Chellappa R (2006) View invariance for human action recognition. Int J Comput Vis 66(1):83–101CrossRef
15.
Rao C, Yilmaz A, Shah M (2002) View-invariant representation and recognition of actions. Int J Comput Vis 50(2):203–226MATHCrossRef
16.
Weinland D, Boyer E, Ronfard R (2007) Action recognition from arbitrary views using 3D exemplars. In: IEEE international conference on computer vision, pp 1–7
17.
Farhadi A, Tabrizi M (2008) Learning to recognize activities from the wrong view point. In: European conference on computer vision, pp 154–166
18.
Liu J, Shah M, Kuipers B, Savarese S (2011) Cross-view action recognition via view knowledge transfer. In: IEEE conference on computer vision and pattern recognition, pp 3209–3216
19.
Zheng J, Jiang Z, Phillips P, Chellappa R (2012) Cross-view action recognition via a transferable dictionary pair. In: British machine vision conference, pp 7–16
20.
Li B, Camps O, Sznaier M (2012) Cross-view activity recognition using hankelets. In: IEEE conference on computer vision and pattern recognition, pp 1362–1369
21.
Zhang Z, Wang C, Xiao B, Zhou W, Liu S, Shi C (2013) Cross-view action recognition via a continuous virtual path. In: IEEE conference on computer vision and pattern recognition, pp 2690–2697
22.
Farhadi A, Endres I, Hoiem D, Forsyth D (2009) Describing objects by their attributes. In: IEEE conference on computer vision and pattern recognition, pp 1778–1785
23.
Lampert C, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. In: IEEE conference on computer vision and pattern recognition, pp 951–958
24.
Vaquero D, Feris R, Tran D, Brown L, Hampapur A, Turk M (2009) Attribute-based people search in surveillance environments. In: IEEE workshop on applications of computer vision, pp 1–8
25.
Liu J, Kuipers B, Savarese S (2011) Recognizing human actions by attributes. In: IEEE conference on computer vision and pattern recognition, pp 3337–3344
26.
Kumar N, Berg A, Belhumeur P, Nayar S (2009) Attribute and simile classifiers for face verification. In: IEEE international conference on computer vision, pp 365–372
27.
Wang Y, Mori G (2010) A discriminative latent model of object classes and attributes. In: European conference on computer vision, pp 155–168
28.
Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2012) Human action recognition with attribute regularization. In: IEEE international conference on advanced video and signal-based surveillance, pp 112–117
29.
Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2013) Attribute regularization based human action recognition. IEEE Trans Inf Forensics Secur 8(10):1600–1609CrossRef
30.
Parikh D, Grauman K (2011) Relative attributes. In: IEEE international conference on computer vision, pp 503–510
31.
Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2014) Robust relative attributes for human action recognition. Pattern Anal Appl. doi:10.1007/s10044-013-0349-3
32.
33.
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE conference on computer vision and pattern recognition, pp 886–893
34.
Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: European conference on computer vision, pp 428–441
35.
Tran D, Sorokin A (2008) Human activity recognition with metric learning. In: European conference on computer vision, pp 548–561
36.
Le Q, Zou W, Yeung S, Ng A (2011) Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: IEEE conference on computer vision and pattern recognition, pp 3361–3368
37.
Perronnin F, Sánchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: European conference on computer vision, pp 143–156
38.
Hwang S, Sha F, Grauman K (2011) Sharing features between objects and their attributes. In: IEEE conference on computer vision and pattern recognition, pp 1761–1768
Metadaten
Titel
Semantic Analysis in Human Action Recognition: A Comprehensive Study
verfasst von
Zhong Zhang
Shuang Liu
Shuaiqi Liu
Liang Han
Yunxue Shao
Copyright-Jahr
2015
Buch
The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems

Print ISBN: 978-3-319-08990-4

Electronic ISBN: 978-3-319-08991-1

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-08991-1_59

Neuer Inhalt

    Bildnachweise