Advertisement

Visual Saliency Based Object Tracking

  • Geng Zhang
  • Zejian Yuan
  • Nanning Zheng
  • Xingdong Sheng
  • Tie Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5995)

Abstract

This paper presents a novel method of on-line object tracking with the static and motion saliency features extracted from the video frames locally, regionally and globally. When detecting the salient object, the saliency features are effectively combined in Conditional Random Field (CRF). Then Particle Filter is used when tracking the detected object. Like the attention shifting mechanism of human vision, when the object being tracked disappears, our tracking algorithm can change its target to other object automatically even without re-detection. And different from many other existing tracking methods, our algorithm has little dependence on the surface appearance of the object, so it can detect any category of objects as long as they are salient, and the tracking is robust to the change of global illumination and object shape. Experiments on video clips of various objects show the reliable results of our algorithm.

Keywords

Gaussian Mixture Model Object Tracking Salient Object Conditional Random Field Visual Saliency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to Detect A Salient Object. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2007)Google Scholar
  2. 2.
    Liu, T., Zheng, N.N., Ding, W., Yuan, Z.J.: Video Attention: Learning to Detect A Salient Object Sequence. In: 19th International Conference on Pattern Recognition (2008)Google Scholar
  3. 3.
    Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  4. 4.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRefGoogle Scholar
  5. 5.
    Rasmussen, C., Hager, G.D.: Probabilistic Data Association Methods for Tracking Complex Visual Objects. IEEE Trans. Pattern Analysis Machine Intell. 23(6), 560–576 (2001)CrossRefGoogle Scholar
  6. 6.
    Hager, G.D., Hager, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Analysis Machine Intell. 20(10), 1025–1039 (1998)CrossRefGoogle Scholar
  7. 7.
    Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, New York (2001)zbMATHGoogle Scholar
  8. 8.
    Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Proc. European Conf. on Computer Vision, vol. 1, pp. 343–356 (1996)Google Scholar
  9. 9.
    Leymarie, F., Levine, M.: Tracking deformable objects in the plane using an active contour model. IEEE Trans. Pattern Analysis Machine Intell. 15(6), 617–634 (1993)CrossRefGoogle Scholar
  10. 10.
    Carmi, R., Itti, L.: Visual causes versus correlates of attentional selection in dynamic scenes. Vision Research 46(26), 4333–4345 (2006)CrossRefGoogle Scholar
  11. 11.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Analysis Machine Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  12. 12.
    Felzenszwalb, P.F., Huttenlocher, D.F.: Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  13. 13.
    Smith, S.M., Brady, J.M.: ASSET-2: Real-Time Motion Segmentation and Shape Tracking. IEEE Trans. Pattern Analysis Machine Intell. 17(8), 814–820 (1995)CrossRefGoogle Scholar
  14. 14.
    Bouguet, J.Y.: Pyramidal Implementation of the Lucas-Kanade Feature Tracker. Tech. Rep., Intel Corporation, Microprocessor Research Labs (1999)Google Scholar
  15. 15.
    Collins, R.T., Liu, Y.: On-Line Selection of Discriminative Tracking Features. In: Proc. IEEE Conf. on Computer Vision (2003)Google Scholar
  16. 16.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proc. IEEE Conf. on Computer Vision (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Geng Zhang
    • 1
  • Zejian Yuan
    • 1
  • Nanning Zheng
    • 1
  • Xingdong Sheng
    • 1
  • Tie Liu
    • 2
  1. 1.Institution of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityChina
  2. 2.IBM China Research Lab 

Personalised recommendations