Skip to main content
Erschienen in: Soft Computing 12/2014

01.12.2014 | Methodologies and Application

A probabilistic object tracking model based on condensation algorithm

verfasst von: Muammer Catak

Erschienen in: Soft Computing | Ausgabe 12/2014

Einloggen

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

search-config
loading …

Abstract

Object tracking, which has many application in our daily life, is an important topic in electronics engineering area. It basically deals with estimation and location of an object in given video frames. In this paper, a novel object tracking algorithm based on particle filtering associate with population balances is proposed. The developed algorithm was used to track objects in synthetic frames and natural video frames. According to results, it has high accuracy level for single and multi-object tracking.

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!

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!

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!

Literatur
Zurück zum Zitat Amini A, Kamilov US, Bostan E, Unser M (2013) Bayesian estimation for continuous-time Sparse stochastic processes. Signal Process IEEE Trans 61(4):907–920CrossRefMathSciNet Amini A, Kamilov US, Bostan E, Unser M (2013) Bayesian estimation for continuous-time Sparse stochastic processes. Signal Process IEEE Trans 61(4):907–920CrossRefMathSciNet
Zurück zum Zitat Boltzmann L (1872) Weitere studien ber das wrmegleichgewicht unter gas-moleculen. Wien Ber 66:275. Translation in english can be found. In: Brush SG (ed), Selected Readings in Physics, Kinetic Theory, vol. 2: Irreversible processes, Pergamon, Newyork (1966) Boltzmann L (1872) Weitere studien ber das wrmegleichgewicht unter gas-moleculen. Wien Ber 66:275. Translation in english can be found. In: Brush SG (ed), Selected Readings in Physics, Kinetic Theory, vol. 2: Irreversible processes, Pergamon, Newyork (1966)
Zurück zum Zitat Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2009) Robust tracking by detection using a detector confidence particle filter. In: IEEE 12th International Conference on Computer Vision, pp 1515–1522 Breitenstein MD, Reichlin F, Leibe B, Koller-Meier E, Van Gool L (2009) Robust tracking by detection using a detector confidence particle filter. In: IEEE 12th International Conference on Computer Vision, pp 1515–1522
Zurück zum Zitat Djuric PM, Kotecha JH, Zhang J, Huang Y, Ghirmai T, Bugallo MF, Miguez J (2003) Particle filtering. IEEE Signal Process Mag 20(5):19–38CrossRef Djuric PM, Kotecha JH, Zhang J, Huang Y, Ghirmai T, Bugallo MF, Miguez J (2003) Particle filtering. IEEE Signal Process Mag 20(5):19–38CrossRef
Zurück zum Zitat Fotouhi M, Gholami AR, Kasaei S (2011) Particle filter-based object tracking using adaptive histogram. In: Proceedings of 7th Iranian Machine Vision and Image Processing (MVIP), pp 1–5 Fotouhi M, Gholami AR, Kasaei S (2011) Particle filter-based object tracking using adaptive histogram. In: Proceedings of 7th Iranian Machine Vision and Image Processing (MVIP), pp 1–5
Zurück zum Zitat Gencaga D, Ertuzun A, Kuruoglu EE (2008) Modeling of non-stationary autoregressive alpha-stable processes by particle filters. Digit Signal Process 18(3):465–478CrossRef Gencaga D, Ertuzun A, Kuruoglu EE (2008) Modeling of non-stationary autoregressive alpha-stable processes by particle filters. Digit Signal Process 18(3):465–478CrossRef
Zurück zum Zitat Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to non-linear/non-Gaussian Bayesian state estimation. IEE Proc F 140(2):107–113 Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to non-linear/non-Gaussian Bayesian state estimation. IEE Proc F 140(2):107–113
Zurück zum Zitat Jacquot A, Sturm P, Ruch O (2005) Adaptive tracking of non-rigid objects based on color histograms and automatic parameter selection, Workshop on Motion and Video Computing (WACV/MOTIONS), vol 2, pp 103–109 Jacquot A, Sturm P, Ruch O (2005) Adaptive tracking of non-rigid objects based on color histograms and automatic parameter selection, Workshop on Motion and Video Computing (WACV/MOTIONS), vol 2, pp 103–109
Zurück zum Zitat Kolmogorov AN (1941) Stationary sequences in Hilbert spaces. Bull Math Univ Moscow 2(6):40 Kolmogorov AN (1941) Stationary sequences in Hilbert spaces. Bull Math Univ Moscow 2(6):40
Zurück zum Zitat Li Y, Haizhou A, Yamashita T, Lao S, Kawade M (2008) Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans Pattern Anal Mach Intell 30(10):1728–1740CrossRef Li Y, Haizhou A, Yamashita T, Lao S, Kawade M (2008) Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans Pattern Anal Mach Intell 30(10):1728–1740CrossRef
Zurück zum Zitat Nummiaro K, Koller-Meier E, Van Gool L (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110 Nummiaro K, Koller-Meier E, Van Gool L (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110
Zurück zum Zitat Rosenbluth MN, Rosenbluth AW (1956) Monte Carlo calculation of the average extension of molecular chains. J Chem Phys 23(2):356–359 Rosenbluth MN, Rosenbluth AW (1956) Monte Carlo calculation of the average extension of molecular chains. J Chem Phys 23(2):356–359
Zurück zum Zitat van Leeuwen PJ (2010) Nonlinear data assimilation in geosciences: an extremely efficient particle filter. QJR Meteorol Soc 136:1991–1999CrossRef van Leeuwen PJ (2010) Nonlinear data assimilation in geosciences: an extremely efficient particle filter. QJR Meteorol Soc 136:1991–1999CrossRef
Zurück zum Zitat Wang TH, Chang JY, Chen LG (2009) Algorithm and architecture for object tracking using particle filter. In: International Conference on Multimedia and Expo (ICME), pp 1374–1377 Wang TH, Chang JY, Chen LG (2009) Algorithm and architecture for object tracking using particle filter. In: International Conference on Multimedia and Expo (ICME), pp 1374–1377
Zurück zum Zitat Wiener N (1949) Extrapolation, interpolation and smoothing of time series with engineering applications. Wiley, New York Wiener N (1949) Extrapolation, interpolation and smoothing of time series with engineering applications. Wiley, New York
Zurück zum Zitat Zivkovic Z, Cemgil AT, Krose B (2009) Approximate Bayesian methods for kernel-based object tracking. Comput Vis Image Underst 113(6):743–749CrossRef Zivkovic Z, Cemgil AT, Krose B (2009) Approximate Bayesian methods for kernel-based object tracking. Comput Vis Image Underst 113(6):743–749CrossRef
Zurück zum Zitat Zivkovic Z, Krose B (2004) An EM-like algorithm for color histogram-based object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Zivkovic Z, Krose B (2004) An EM-like algorithm for color histogram-based object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Metadaten
Titel
A probabilistic object tracking model based on condensation algorithm
verfasst von
Muammer Catak
Publikationsdatum
01.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 12/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1215-3

Weitere Artikel der Ausgabe 12/2014

Soft Computing 12/2014 Zur Ausgabe