Journal of Zhejiang University-SCIENCE A

, Volume 10, Issue 10, pp 1476–1482 | Cite as

Embedding ensemble tracking in a stochastic framework for robust object tracking

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Abstract

We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion, illumination changes, and abrupt motion. It operates on likelihood images generated by the ensemble method, and combines mean shift and particle filtering in a principled way, where a better proposal distribution is designed by first propagating particles via a motion model, and then running mean shift to move towards their local peaks in the likelihood image. An observation model in the particle filter incorporates global and local information within a region, and an adaptive motion model is adopted to depict the evolution of the object state. The algorithm needs fewer particles to manage the tracking task compared with the general particle filter, and recaptures the object quickly after occlusion occurs. Experiments on two image sequences demonstrate the effectiveness and robustness of the proposed algorithm.

Key words

Ensemble tracking Particle filter Mean shift Likelihood mean 

CLC number

TP317.4 

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References

  1. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T., 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 50(2):174–188. [doi:10.1109/78.978374]CrossRefGoogle Scholar
  2. Avidan, S., 2007. Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell., 29(2):261–271. [doi:10.1109/TPAMI.2007.35]CrossRefGoogle Scholar
  3. Bradski, G.R., 1998. Real Time Face and Object Tracking as a Component of a Perceptual User Interface. Proc. 4th IEEE Workshop on Applications of Computer Vision, p.214–219.Google Scholar
  4. Collins, R.T., Liu, Y., Leordeanu, M., 2005. Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell., 27(10):1631–1643. [doi:10.1109/TPAMI.2005.205]CrossRefGoogle Scholar
  5. Comaniciu, D., Ramesh, V., Meer, P., 2003. Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell., 25(5):564–577. [doi:10.1109/TPAMI.2003.1195991]CrossRefGoogle Scholar
  6. Dalai, N., Triggs, B., 2005. Histograms of Oriented Gradients for Human Detection. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1:886–893. [doi:10.1109/CVPR.2005.177]Google Scholar
  7. Freund, Y., Schapire, R.E., 1996. Experiments with a New Boosting Algorithm. Proc. 13th Int. Conf. on Machine Learning, p.148–156.Google Scholar
  8. Friedman, J., Hastie, T., Tibshirani, R., 2000. Additive logistic regression: a statistical view of boosting. Ann. Statist., 28(2):337–374. [doi:10.1214/aos/1016218223]MathSciNetCrossRefMATHGoogle Scholar
  9. Isard, M., Blake, A., 1998a. CONDENSATION: conditional density propagation for visual tracking. Int. J. Comput. Vis., 29(1):5–28. [doi:10.1023/A:1008078328650]CrossRefGoogle Scholar
  10. Isard, M., Blake, A., 1998b. ICONDENSATION: Unifying Low-level and High-level Tracking in a Stochastic Framework. Proc. 5th European Conf. on Computer Vision, 1406:893–908.Google Scholar
  11. Jepson, A.D., Fleet, D.J., El-Maraghi, T.F., 2003. Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell., 25(10):1296–1311. [doi:10.1109/TPAMI.2003.1233903]CrossRefGoogle Scholar
  12. Maggio, E., Cavallaro, A., 2005. Hybrid Particle Filter and Mean Shift Tracker with Adaptive Transition Model. Proc. IEEE Conf. on Acoustics, Speech, and Signal Processing, 2:221–224.Google Scholar
  13. Odobez, J.M., Gatica-Perez, D., Ba, S.O., 2006. Embedding motion in model-based stochastic tracking. IEEE Trans. Image Process., 15(11):3514–3530. [doi:10.1109/TIP.2006.877497]CrossRefGoogle Scholar
  14. Perez, P., Hue, C., Vermaak, J., Gangnet, M., 2002. Color-based Probabilistic Tracking. Proc. 7th European Conf. on Computer Vision, 2350:661–675.MATHGoogle Scholar
  15. Shan, C.F., Tan, T.N., Wei, Y.C., 2007. Real-time hand tracking using a mean shift embedded particle filter. Pattern Recogn., 40(7):1958–1970. [doi:10.1016/j.patcog.2006.12.012]CrossRefMATHGoogle Scholar
  16. Yilmaz, A., Javed, O., Shah, M., 2006. Object tracking: a survey. ACM Comput. Surv., 38(4):Article No. 13, p.1–45. [doi:10.1145/1177352.1177355]CrossRefGoogle Scholar
  17. Zhou, S.K., Chellappa, R., Moghaddam, B., 2004. Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Process., 13(11):1491–1506. [doi:10.1109/TIP.2004.836152]CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Institute of Industrial Process ControlZhejiang UniversityHangzhouChina

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