Semi-Automatic Object Tracking in Video Sequences by Extension of the MRSST Algorithm

  • Marko Esche
  • Mustafa Karaman
  • Thomas Sikora
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 158)

Abstract

The objective of this work is to investigate a new approach for segmentation of real-world objects in video sequences. While some amount of user interaction is still necessary for most algorithms in this field, in order for them to produce adequate results, these can be reduced making use of certain properties of graph-based image segmentation algorithms. Based on one of these algorithms a framework is proposed that tracks individual foreground objects through arbitrary video sequences and partly automates the necessary corrections required from the user. Experimental results suggest that the proposed algorithm performs well on both low- and high-resolution video sequences and can even, to a certain extent, cope with motion blur and gradual object deformations.

Keywords

Image segmentation Object tracking Binary partition tree 

References

  1. 1.
    Adamek T, O’Connor NE (2006) Interactive object contour extraction for shape modeling. In: 1st international workshop on shapes and semantics, vol 1(1). pp 31–39Google Scholar
  2. 2.
    Adamek T, O’Connor NE (2007) Using dempster-shafer theory to fuse mulitple information sources in region-based segmentation. In: Proceedings of the 14th international converence on image processing ICIP, vol 2. pp 269–272Google Scholar
  3. 3.
    Adamek T, O’Connor NE, Murphy N (2005) Region-based segmentation of images using syntactic visual features. In: Proceedings of the 6th international workshop on image analysis for multimedia interactive services (WIAMIS)Google Scholar
  4. 4.
    Alatan A, Onural L, Wollborn M, Mech R, Tuncel E, Sikora T (1998) Image sequence analysis for emerging interactive multimedia services—the european cost 211 framework. IEEE Trans. Circuits Syst. Video Technol. 8:802–813CrossRefGoogle Scholar
  5. 5.
    Bai X, Wang J, Simons D, Sapiro G (2009) Video snapcut: robust video object cutout using localized classifiers. In: SIGGRAPH’09: ACM SIGGRAPH 2009 papers, vol 28(3)Google Scholar
  6. 6.
    Cooray S, O’Connor N, Marlow S, Murphy N, Curran T (2001) Semi-automatic video object segmentation using recursive shortest spanning tree and binary partition tree. In: Proceedings of the 3rd international workshop on image analysis for multimedia interactive services (WIAMIS)Google Scholar
  7. 7.
    Corrigan D, Robinson S, Kokaram A (2008) Video matting using motion extended grabcut. In: 5th european conference on visual media production (CMVP), pp 1–9Google Scholar
  8. 8.
    Freixenet J, Munoz X, Raba D, Marti J, Cufi X (2002) Yet another survey on image segmenation: region and boundary information integration. Lect Notes Comput Sci 2352/2002:21–25Google Scholar
  9. 9.
    Krutz A, Glantz A, Borgmann T, Frater M, Sikora T (2009) Motion-based object segmentation using local background sprites. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1221–1224Google Scholar
  10. 10.
    Vilaplana V, Marqués F, Salembier P (2008) Binary partition trees for object detection. IEEE Trans. Image Process. 17:11CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Marko Esche
    • 1
  • Mustafa Karaman
    • 1
  • Thomas Sikora
    • 1
  1. 1.Communication Systems Group, Technische Universität Berlin Sekr. EN1BerlinGermany

Personalised recommendations