Object Detection and Segmentation Using Adaptive MeanShift Blob Tracking Algorithm and Graph Cuts Theory

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 233)

Summary

In this paper, we present method of detection, segmentation and tracking to different objects in video sequence in real-time. We propose new approach based on Blob tracking, the technique, we find a hybrid combination between tracking-detection, in blob tracking use detection model based on two pieces of information; brightness and color. Our approach adds new properties in these blobs based on shape features extractions, where we define several properties for efficient detection. These blobs, present objects detected, the motion is estimated by non-parametric Kernel density estimation by using MeanShift algorithm to track this blobs. Segmentation is performed by GraphCuts approach; it generates and updates a set of Blobs in the sequence. Experimental results demonstrate that our method is robust for challenging data and present many advantages inside other approaches.

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References

  1. 1.
    Comaniciu, D., Ramesh, V., Meer, P.: Real Time Tracking of Non-Rigid Objects using Mean Shift. In: IEEE Computer Vision and Pattern Recognition II, pp. 142–149 (2000)Google Scholar
  2. 2.
    Collins, R.T.: Mean-shift Blob Tracking through Scale Space. In: IEEE, pp. 234–240 (2003)Google Scholar
  3. 3.
    Liang, D., Huang, Q., Jiang, S., Yao, H., Gao, H.: Mean shift blob tracking with kernel histogram filtering and hypothesis testing, pp. 605–614 (2005)Google Scholar
  4. 4.
    Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. on Information Theory 21(1), 32–40 (1975)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. II, pp. 142–149 (2000)Google Scholar
  6. 6.
    Liang, D., Huang, Q., Jiang, S., Yao, H., Gao, W.: Mean-Shift Blob Tracking with Adaptive Feature Selection and Scale Adaptation. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 13(3), pp. 369–372 (2007)Google Scholar
  7. 7.
    Zhao, L., An, G., Zhang, F., Wang, H., Dai, G.: Scale Adaptation of Mean Shift Based on Graph Cuts Theory. In: 12th International Conference on Computer-Aided Design and Computer Graphics, pp. 202–205. Chinese Academy of Sciences (September 2011)Google Scholar
  8. 8.
    Bugeau, A., Pérez, P.: Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects with Graph Cuts. EURASIP Journal on Image and Video Processing, Ref: 2008:317278 (2008)Google Scholar
  9. 9.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions Pattern Analysis and Machine Intelligence 123, 1222–1239 (2001)CrossRefGoogle Scholar
  10. 10.
    Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.LIAD. Faculty of SciencesHassan II UniversityMaarifMorocoo

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