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Fast Correction Visual Tracking via Feedback Mechanism

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Intelligence Science and Big Data Engineering. Image and Video Data Engineering (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9242))

Abstract

Visual tracking is a fundamental problem in computer vision field. Most online visual trackers focus on the appearance information and inference theory to realize tracking frame by frame. However, enough attention has not been paid to the correction ability of a tracking system, which leads to drift problems or tracking failures in previous works. This paper investigates the contribution of feedback mechanism in a tracking-by-detection framework. Results indicate that the changing values of the target state’s posterior distributions provide superior information to the connection between tracking result and the ground truth. We further analyse the spatial appearance information and propose an adaptive feedback tracking method using Discrete-Quaternion-Fourier-Transform (DQFT). Taking advantages of the stability of closed-loop control and the efficiency of DQFT, the proposed tracker can make a distinction between the easy-tracking frames and the hard-tracking frames, and then re-track hard-tracking frames using further temporal information to realize the correction ability. Experiments over 50 challenging videos demonstrate the effectiveness and robustness of the tracker, and the resulting tracker outperforms the existing state-of-the-art methods.

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References

  1. Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: A benchmark. In: IEEE CVPR, pp. 2411–2418. IEEE Press, Portland (2013)

    Google Scholar 

  2. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)

    Article  Google Scholar 

  3. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence (1981)

    Google Scholar 

  4. Baker, S., Matthews, I.: Lucas-Kanade 20 Years on: a unifying framework. Int. J. Comput. Vision 56(3), 221–255 (2004)

    Article  Google Scholar 

  5. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE CVPR (2006)

    Google Scholar 

  6. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE CVPR (2000)

    Google Scholar 

  7. Ross, D.A., Lim, J., Lin, R.S.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77, 125–141 (2008)

    Article  Google Scholar 

  8. Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: IEEE ICCV (2009)

    Google Scholar 

  9. Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE CVPR (2009)

    Google Scholar 

  10. Dietterich, T.G., Lathropand, R.H., Lozano-Pez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89, 31–71 (1997)

    Article  MATH  Google Scholar 

  11. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: IEEE CVPR (2010)

    Google Scholar 

  12. Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: IEEE ICCV (2011)

    Google Scholar 

  13. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)

    Google Scholar 

  14. Ell, T.A.: Quaternion Fourier transforms for analysis of 2-dimensional linear-time-invariant partial-diffenrential systems. In: IEEE Conference on Decision and Control, pp. 1830–1841 (1993)

    Google Scholar 

  15. Dimitrov, V.S., Cooklev, T.V., Donevsky, B.D.: On the multiplication of reduced biquaternions and applications. Inf. Process. Letters 43, 161–164 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  16. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE CVPR (2010)

    Google Scholar 

  18. Henriques, J.F., Caseiro, R., Martins, P.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)

    Article  Google Scholar 

  19. Danelljan, M., Khan, F.S., Felsberg, M., Weijer, J.: Adaptive color attributes for real-time visual tracking. In: IEEE CVPR, pp. 1090–1097 (2014)

    Google Scholar 

  20. Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 127–141. Springer, Heidelberg (2014)

    Google Scholar 

  21. Briechle, K., Hanebeck, U.D.: Template matching using fast normalized cross correlation. In: SPIE, vol. 4387, pp. 95–102 (2001)

    Google Scholar 

  22. Scholkopf, B., Smola, A.J.: Learning with kernels: Support vector machines, regularization, optimization, and beyond, pp. 125–148. MIT Press (2002)

    Google Scholar 

  23. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE CVPR (2012)

    Google Scholar 

  24. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: IEEE CVPR (2012)

    Google Scholar 

  26. Bao, C., Wu, Y., Ling, H., Ji, H.: Real Time robust L1 tracker using accelerated proximal gradient approach. In: IEEE CVPR (2012)

    Google Scholar 

  27. Wu, Y., Shen, B., Ling, H.: Online robust image alignment via iterative convex optimization. In: IEEE CVPR (2012)

    Google Scholar 

  28. Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: IEEE CVPR (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, 214122, China

    Tianyang Xu & Xiaojun Wu

Authors
  1. Tianyang Xu
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  2. Xiaojun Wu
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Corresponding author

Correspondence to Xiaojun Wu .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Shaanxi, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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© 2015 Springer International Publishing Switzerland

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Xu, T., Wu, X. (2015). Fast Correction Visual Tracking via Feedback Mechanism. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-23989-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23987-3

  • Online ISBN: 978-3-319-23989-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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