Advertisement

Resolving Conflicts in Object Tracking in Video Stream Employing Key Point Matching

  • Grzegorz Szwoch
Part of the Communications in Computer and Information Science book series (CCIS, volume 287)

Abstract

A novel approach to resolving ambiguous situations in object tracking in video streams is presented. The proposed method combines standard tracking technique employing Kalman filters with global feature matching method. Object detection is performed using a background subtraction algorithm, then Kalman filters are used for object tracking. At the same time, SURF key points are detected only in image sections identified as moving objects and stored in trackers. Descriptors of these key points are used for object matching in case of tracking conflicts, for identification of the current position of each tracked object. Results of experiments indicate that the proposed method is useful in resolving conflict situations in object tracking, such as overlapping or splitting objects.

Keywords

video analysis object tracking Kalman filters image matching 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Czyżewski, A., Szwoch, G., Dalka, P., et al.: Multi-Stage Video Analysis Framework. In: Lin, W. (ed.) Video Surveillance, pp. 147–172. InTech, Rijeka (2011)Google Scholar
  2. 2.
    Fukunaga, K., Hostetler, L.D.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Trans. on Information Theory 21, 32–40 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Horn, B.K.P., Schunck, B.G.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  4. 4.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. of Imaging Understanding Workshop, pp. 121–130 (1981)Google Scholar
  5. 5.
    Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-time Tracking. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 246–252 (1999)Google Scholar
  6. 6.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time Foreground-Background Segmentation using Codebook Model. Real-time Imaging 11, 172–185 (2005)CrossRefGoogle Scholar
  7. 7.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Technical report TR 95-041. UNC-Chapel Hill (2006), http://www.cs.unc.edu/~welch/kalman/
  8. 8.
    Szwoch, G., Dalka, P., Czyżewski, A.: Resolving Conflicts in Object Tracking for Automatic Detection of Events in Video. Elektronika 52, 52–55 (2011)Google Scholar
  9. 9.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding 110, 346–359 (2008)CrossRefGoogle Scholar
  10. 10.
    PETS, Benchmark Data (2006), http://www.cvg.rdg.ac.uk/PETS2006/data.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Grzegorz Szwoch
    • 1
  1. 1.Multimedia Systems DepartmentGdansk University of TechnologyGdanskPoland

Personalised recommendations