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:
Towards a Cloud Robotics Platform for Distributed Visual SLAM Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Fire and Smoke Dynamic Textures Characterization by Applying Periodicity Index Based on Motion Features

verfasst von : Kanoksak Wattanachote, Zehang Lin, Mingchao Jiang, Liuwu Li, Gongliang Wang, Wenyin Liu

Erschienen in: Computer Vision Systems

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Dynamic texture has been described as images sequence that demonstrates continuous movement of pixels intensity change patterns in time. We consider the motion features of smoke and fire dynamic textures, which are important for fire calamity surveillance system to analyze the fire situation. We propose a method to understand the motion of intensity change. The objective is not only for classification purpose but also to characterize the motion pattern of fire and smoke dynamic texture. The radius of vector usually describes how fast the intensity change. The motion coherence index has been developed to assess the motion coherency between observed vector and its neighborhoods. We implement strategic motion coherence analysis to determine the motion coherence index of motion vector field in each video frame. In practical, both covariance stationarity of average radius and motion coherence index are efficiently used to investigate fire and smoke characteristics by applying periodicity index for analysis.

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 Pedestrian Detection Over 100 fps with C4 Algorithm
Nächstes Kapitel A Novel Real-Time Gesture Recognition Algorithm for Human-Robot Interaction on the UAV
Literatur
1.
Doretto, G.: Dynamic textures: modeling, learning, synthesis, animation, segmentation, and recognition. Thesis, University of California (2005)
2.
Tsai, J.C., Shih, T.K., Wattanachote, K., Li, K.C.: Video editing using motion inpainting. In: 26th IEEE International Conference on Advanced Information Networking and Applications, pp. 649–654 (2012)
3.
Wattanachote, K., Shih, T.K.: Automatic dynamic texture transformation based on a new motion coherence metric. IEEE Trans. Circuits Syst. Video Technol. 26(10), 1805–1820 (2016)CrossRef
4.
Dmitry, C., Sandor, F.: On motion periodicity of dynamic textures. In: British Machine Vision Conference, vol. 1, pp. 167–176 (2006)
5.
Wattanachote, K., Li, K., Wang, Y., Shih, T.K., Liu, W.Y.: Preliminary investigation on stationarity of dynamic smoke texture and dynamic fire texture based on motion coherent metric. In: International Conference on Machine Vision and Information Technology, pp. 99–104 (2017)
6.
Deretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. Int. J. Comput. Vis. 51(2), 91–109 (2003)CrossRefMATH
7.
Soatto, S., Doretto, G., Wu, Y.N.: Dynamic textures. In: IEEE International Conference on Computer Vision, vol. 2, pp. 439–446 (2001)
8.
Chan, S.C., Zhang, Z.G.: Local polynomial modeling and variable bandwidth selection for time-varying linear systems. IEEE Trans. Instrum. Measur. 60(3), 1102–1117 (2011)CrossRef
9.
Zhang, Z.G., Hung, Y.S., Chan, S.C.: Local polynomial modelling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG. IEEE Trans. Biomed. Eng. 58(3), 557–566 (2011)CrossRef
10.
Chen, C.I., Chen, Y.C.: Comparative study of harmonic and interharmonic estimation methods for stationary and time-varying signals. IEEE Trans. Industr. Electron. 61(1), 397–404 (2014)CrossRef
11.
Zhang, Z.G., Chan, S.C., Wang, C.: A new regularized adaptive windowed lomb-periodogram for time-frequency analysis of non-stationary signals with impulsive components. IEEE Trans. Instrum. Measur. 61(8), 2283–2304 (2012)CrossRef
12.
Saisan, P., Doretto, G., Wu, Y., Soatto, S.: Dynamic texture recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 58–63 (2001)
13.
Chen, T., Kao, C., Chang, S.: An intelligent real-time fire-detection method based on video processing. In: The International Carnahan Conference on Security Technology, pp. 104–111 (2003)
14.
Ha, C., Jeon, G., Jeon, J.: Vision-based smoke detection algorithm for early fire recognition in digital video recording system. In: 7th International Conference on Signal Image Technology and Internet-Based Systems, pp. 209–212 (2011)
15.
Farneback, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: 15th International Conference on Pattern Recognition, vol. 1, pp. 135–139 (2000)
16.
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). doi:10.​1007/​3-540-45103-X_​50 CrossRef
17.
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006). Chapter 4MATH
18.
Franses, P.H.: Time Series Models for Business and Economic Forecasting. Cambridge University Press, Cambridge (1998)
19.
Liu, H.X., Li, J., Zhao, Y., Qu, L.S.: Improved singular value decomposition technique for detecting and extracting periodic impulse component in a vibration signal. Chin. J. Mech. Eng. 17(3), 340–345 (2004)CrossRef
20.
Kanjilal, P.P., Palit, S.: The singular value decomposition-applied in modelling and prediction of quasiperiodic processes. IEEE Trans. Sig. Process. 43(6), 1536–1540 (1995)CrossRefMATH
Metadaten
Titel
Fire and Smoke Dynamic Textures Characterization by Applying Periodicity Index Based on Motion Features
verfasst von
Kanoksak Wattanachote
Zehang Lin
Mingchao Jiang
Liuwu Li
Gongliang Wang
Wenyin Liu
Copyright-Jahr
2017
Buch
Computer Vision Systems

Print ISBN: 978-3-319-68344-7

Electronic ISBN: 978-3-319-68345-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68345-4_45