Advertisement

Kernel-Based Motion-Blurred Target Tracking

  • Yi Wu
  • Jing Hu
  • Feng Li
  • Erkang Cheng
  • Jingyi Yu
  • Haibin Ling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

Motion blurs are pervasive in real captured video data, especially for hand-held cameras and smartphone cameras because of their low frame rate and material quality. This paper presents a novel Kernel-based motion-Blurred target Tracking (KBT) approach to accurately locate objects in motion blurred video sequence, without explicitly performing deblurring. To model the underlying motion blurs, we first augment the target model by synthesizing a set of blurred templates from the target with different blur directions and strengths. These templates are then represented by color histograms regularized by an isotropic kernel. To locate the optimal position for each template, we choose to use the mean shift method for iterative optimization. Finally, the optimal region with maximum similarity to its corresponding template is considered as the target. To demonstrate the effectiveness and efficiency of our method, we collect several video sequences with severe motion blurs and compare KBT with other traditional trackers. Experimental results show that our KBT method can robustly and reliably track strong motion blurred targets.

Keywords

Target Model Color Histogram Visual Tracking Motion Blur Blur Kernel 
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.
    Badrinarayanan, V., Pérez, P., Clerc, F.L., Oisel, L.: Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues. In: IEEE International Conference on Computer Vision, ICCV (2007)Google Scholar
  2. 2.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 25, 564–577 (2003)CrossRefGoogle Scholar
  3. 3.
    Cai, J., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009)Google Scholar
  4. 4.
    Dai, S., Yang, M., Wu, Y., Katsaggelos, A.: Tracking Motion-Blurred Targets in Video. In: IEEE International Conference on Image Processing, ICIP (2006)Google Scholar
  5. 5.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S., Freeman, W.: Removing camera shake from a single photograph. ACM T. on Graphics, SIGGRAPH (2006)Google Scholar
  6. 6.
    Isard, M., Blake, A.: Condensation-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision (IJCV) 29, 5–28 (1998)CrossRefGoogle Scholar
  7. 7.
    Jin, H., Favaro, P., Cipolla, R.: Visual Tracking in the Presence of Motion Blur. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2005)Google Scholar
  8. 8.
    Levin, A.: Blind motion deblurring using image statistics Advances. In: Advances in Neural Information Processing Systems, NIPS (2007)Google Scholar
  9. 9.
    Levin, A., Fergus, R., Durand, F., Freeman, W.: Image and depth from a conventional camera with a coded aperture. ACM T. on Graphics, SIGGRAPH (2007)Google Scholar
  10. 10.
    Lou, Y., Bertozzi, A., Soatto, S.: Direct Sparse Deblurring. Int’l. J. Math. Imaging and Vision (2010)Google Scholar
  11. 11.
    Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum Error Bounded Efficient ℓ1 Tracker with Occlusion Detection. In: CVPR (2011)Google Scholar
  12. 12.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Wu, Y., Wu, B., Liu, J., Lu, H.Q.: Probabilistic Tracking on Riemannian Manifolds. In: IEEE International Conference on Pattern Recognition, ICPR (2008)Google Scholar
  14. 14.
    Wu, Y., Wang, J.Q., Lu, H.Q.: Robust Bayesian tracking on Riemannian manifolds via fragments-based representation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (2009)Google Scholar
  15. 15.
    Wu, Y., Cheng, J., Wang, J.Q., Lu, H.Q.: Real-time visual tracking via incremental covariance tensor learning. In: IEEE International Conference on Computer Vision, ICCV (2009)Google Scholar
  16. 16.
    Richardson, W.: Bayesian-Based Iterative Method of Image Restoration. Journal of the Optical Society of America (JOSA) 62, 55–59 (1972)CrossRefGoogle Scholar
  17. 17.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yi Wu
    • 1
    • 2
    • 3
  • Jing Hu
    • 5
  • Feng Li
    • 4
  • Erkang Cheng
    • 3
  • Jingyi Yu
    • 4
  • Haibin Ling
    • 3
  1. 1.Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science & TechnologyNanjingChina
  2. 2.School of Computer & SoftwareNanjing University of Information Science & TechnologyNanjingChina
  3. 3.Center for Information Science and Technology, Computer and Information Science DepartmentTemple UniversityPhiladelphiaUSA
  4. 4.Department of Computer and Information SciencesUniversity of DelawareNewarkUSA
  5. 5.Network CenterNanjing University of Information Science & TechnologyNanjingChina

Personalised recommendations