Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models

  • Katharina Quast
  • André Kaup
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 158)


GMM-SAMT, a new object tracking algorithm based on a combination of the mean shift principal and Gaussian mixture models (GMMs) is presented. GMM-SAMT stands for Gaussian mixture model based shape adaptive mean shift tracking. Instead of a symmetrical kernel like in traditional mean shift tracking, GMM-SAMT uses an asymmetric shape adapted kernel which is retrieved from an object mask. During the mean shift iterations the kernel scale is altered according to the object scale, providing an initial adaptation of the object shape. The final shape of the kernel is then obtained by segmenting the area inside and around the adapted kernel into object and non-object segments using Gaussian mixture models.


Object tracking Mean shift tracking Gaussian mixture models 



This work has been supported by the Gesellschaft für Informatik, Automatisierung und Datenverarbeitung (iAd) and the Bundesministerium für Wirtschaft und Technologie (BMWi), ID 20V0801I.


  1. 1.
    Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE conference on computer vision and pattern recognition, IEEE Press, New York, pp 142–149Google Scholar
  2. 2.
    Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24:603–619CrossRefGoogle Scholar
  3. 3.
    Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17:790–799CrossRefGoogle Scholar
  4. 4.
    Bradski GR (1998) Computer vision face tracking for use in a perceptual user interface. Intel Technol J 2:12–21Google Scholar
  5. 5.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–575CrossRefGoogle Scholar
  6. 6.
    Collins RT (2003) Mean-shift blob tracking through scale space. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 234–240Google Scholar
  7. 7.
    Qifeng Q, Zhang D, Peng Y (2007) An adaptive selection of the scale and orientation in kernel based tracking. In: Proceedings of the third international IEEE conference on signal-image technologies and internet-based system, IEEE Press, New York, pp 659–664Google Scholar
  8. 8.
    Vilaplana V, Marques F (2008) Region-based mean shift tracking: application to face tracking. In: Proceedings of 15th IEEE international conference on image processing, IEEE Press, New York, pp 2712–2715Google Scholar
  9. 9.
    Yilmaz A (2007) Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection. In: Proceedings of IEEE conference on computer vision and pattern recognition, IEEE Press, New York, pp 1–6Google Scholar
  10. 10.
    Quast K, Kaup A (2009) Scale and shape adaptive mean shift object tracking in video sequences. In: Proceedings 17th European signal processing conference, pp 1513–1517Google Scholar
  11. 11.
    Nowak A, Hörchens L, Röder J, Erdmann M (2006) Colourbased video segmentation for tv studio applications. In: Proceedings of the 51st international scientific colloquium, 2006Google Scholar
  12. 12.
    Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757CrossRefGoogle Scholar
  13. 13.
    Quast K, Kaup A (2010) Real-time moving object detection in video sequences using spatio-temporal adaptive Gaussian mixture models. In: Proceedings of international conference on computer vision theory and applications (VISAPP ’10), Angers, France, 2010Google Scholar
  14. 14.
    Dempster AP, Laird NM, Rubin DB et al (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc. Series B (Methodological) 39(1):1–38Google Scholar
  15. 15.
    Ihlow A, Heuberger A (2009) Sky detection in fisheye images for photogrammetric analysis of the land mobile satellite channel. In: Proceedings of the 10th workshop digital broadcasting, pp 56–60Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Chair of Multimedia Communications and Signal ProcessingUniversity of Erlangen-NurembergErlangenGermany

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