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Long-term tracking based on spatio-temporal context

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Abstract

Aiming at the problem that the fast tracking algorithm using spatio-temporal context (STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene.

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References

  1. YILMAZ A, JAVAD O, SHAH M. Object tracking: A survey [J]. ACM Computing Surveys, 2006, 38(4): 1–45.

    Article  Google Scholar 

  2. KIM D Y, JEON M. Spatio-temporal auxiliary particle filtering with l1-norm based appearance model learning for robust visual tracking [J]. IEEE Transactions on Image Processing, 2013, 22(2): 511–522.

    Article  MathSciNet  Google Scholar 

  3. KALAL Z, MIKOLAJCZYK K, MATAS J. Trackinglearning-detection [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 34(7): 1409–1422.

    Article  Google Scholar 

  4. HUA Y, ALAHARI K, SCHMID C. Occlusion and motion reasoning for long-term tracking [C]//ECCV 2014: European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 172–187.

    Google Scholar 

  5. PERNICI F. Facehugger: The ALIEN tracker applied to faces [C]//ECCV 2012: European Conference on Computer Vision. Florence, Italy: Springer, 2012: 597–601.

    Google Scholar 

  6. SUPANCIC J S, RAMANAN D. Self-paced learning for long-term tracking [C]//CVPR 2013: Computer Vision and Pattern Recognition. Portland, Oregon, USA: IEEE, 2013: 2379–2386.

    Chapter  Google Scholar 

  7. HARE S, SAFFARI A, TORR P H. Struck: Structured output tracking with kernels [J]. International Conference on Computer Vision, 2011, 23(5): 263–270.

    Google Scholar 

  8. WU Y, LIM J, YANG M H. Online object tracking: A benchmark [C]//CVPR 2013: Computer Vision and Pattern Recognition. Portland, Oregon, USA: IEEE, 2013: 2411–2418.

    Chapter  Google Scholar 

  9. ZHANG K H, ZHANG L, LIU Q S, et al. Fast visual tracking via dense spatio-temporal context learning [C]// ECCV 2014: European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 127–141.

    Google Scholar 

  10. LEWIS J P. Fast normalized cross-correlation [J]. Circuits, Systems and Signal Processing, 2009, 28(6): 819–843.

    Article  MATH  Google Scholar 

  11. ROSS D A, LIM J, LIN R S, et al. Incremental learning for robust visual tracking [J]. International Journal of Computer Vision, 2008, 77: 125–141.

    Article  Google Scholar 

  12. GRABNER H, GRABNER M, BISCHOF H. Realtime tracking via on-line boosting [C]//Proceedings of the British Machine Vision Conference 2006. Edinburgh, UK: BMVA, 2006: 47–56.

    Google Scholar 

  13. ADAM A, RIVLIIN E, SHIMSHONI I. Robust fragments-based tracking using the integral histogram [C]//CVPR 2006: Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006: 798–805.

    Google Scholar 

  14. ORON S, BAR-HILLEL A, LEVI D, et al. Locally orderless tracking [J]. International Journal of Computer Vision, 2015, 111(2): 213–228.

    Article  MathSciNet  Google Scholar 

  15. ZHANG T Z, GHANEM B, LIU S, et al. Robust visual tracking via multi-task sparse learning [C]//CVPR 2012: Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012: 2042–2049.

    Chapter  Google Scholar 

  16. BABENKO B, YANG M H, BELONGIE S. Robust object tracking with online multiple instance learning [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(8): 1619–1632.

    Article  Google Scholar 

  17. ZHANG K H, ZHANG L, YANG M H. Real-time compressive tracking [C]//ECCV 2012: European Conference on Computer Vision. Florence, Italy: Springer, 2012: 864–877.

    Chapter  Google Scholar 

  18. COLLINS R T. Mean-shift blob tracking through scale space [C]//CVPR 2003: Computer Vision and Pattern Recognition. Madison, USA: IEEE, 2003: 234–240.

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Jiahui Lu  (陆佳辉), Yimin Chen  (陈一民), Yibo Zou  (邹一波) & Guozhi Zou  (邹国志)

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  1. Jiahui Lu  (陆佳辉)
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  2. Yimin Chen  (陈一民)
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  3. Yibo Zou  (邹一波)
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  4. Guozhi Zou  (邹国志)
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Corresponding author

Correspondence to Yimin Chen  (陈一民).

Additional information

Foundation item: the International Collaborative Research Program of Shanghai Science and Technology Committee (No. 12510708400) and the Summit Filmology Program of Shanghai University in 2015 (No. n.13- a303-15-w23)

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Lu, J., Chen, Y., Zou, Y. et al. Long-term tracking based on spatio-temporal context. J. Shanghai Jiaotong Univ. (Sci.) 22, 504–512 (2017). https://doi.org/10.1007/s12204-017-1863-z

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  • DOI: https://doi.org/10.1007/s12204-017-1863-z

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