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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 320–335Cite as

Landmark-Based Inductive Model for Robust Discriminative Tracking

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

Abstract

The appearance of an object could be continuously changing during tracking, thereby being not independent identically distributed. A good discriminative tracker often needs a large number of training samples to fit the underlying data distribution, which is impractical for visual tracking. In this paper, we present a new discriminative tracker via the landmark-based inductive model (Lim) that is non-parametric and makes no specific assumption about the sample distribution. With an undirected graph representation of samples, the Lim locally approximates the soft label of each sample by a linear combination of labels on its nearby landmarks. It is able to effectively propagate a limited amount of initial labels to a large amount of unlabeled samples. To this end, we introduce a local landmarks approximation method to compute the cross-similarity matrix between the whole data and landmarks. And a soft label prediction function incorporating the graph Laplacian regularizer is used to diffuse the known labels to all the unlabeled vertices in the graph, which explicitly considers the local geometrical structure of all samples. Tracking is then carried out within a Bayesian inference framework where the soft label prediction value is used to construct the observation model. Both qualitative and quantitative evaluations on \(65\) challenging image sequences including the benchmark dataset and other public sequences demonstrate that the proposed algorithm outperforms the state-of-the-art methods.

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References

  1. Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., Hengel, A.V.D.: A survey of appearance models in visual object tracking. ACM Trans. Intell. Syst. Technol. (TIST) 4, 58 (2013)

    Google Scholar 

  2. Jia, X., Lu, H., Yang, M.: Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.1822–1829 (2012)

    Google Scholar 

  3. Wang, D., Lu, H., Yang, M.H.: Least soft-thresold squares tracking. In: Proceedingsof IEEE Conference on Computer Vision and Pattern Recognition, pp. 2371–2378 (2013)

    Google Scholar 

  4. Kwon, J., Lee, K.: Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276 (2010)

    Google Scholar 

  5. Mei, X., Ling, H.: Robust visual tracking using \(\ell 1\) minimization. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1–8 (2009)

    Google Scholar 

  6. Ross, D., Lim, J., Lin, R., Yang, M.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77, 125–141 (2008)

    Article  Google Scholar 

  7. Wu, Y., Ma, B.: Learning distance metric for object contour tracking. Pattern Anal. Appl. 17, 265–277 (2014)

    Article  MathSciNet  Google Scholar 

  8. Zhuang, B., Lu, H., Xiao, Z., Wang, D.: Visual tracking via discriminative sparse similarity map. IEEE Trans. Image Process. 23, 1872–1881 (2014)

    Article  MathSciNet  Google Scholar 

  9. Grabner, H., Grabner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Yang, M., Yuan, J., Wu, Y.: Spatial selection for attentional visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)

    Google Scholar 

  11. Babenko, B., Yang, M., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach.Intell. 33, 1619–1632 (2011)

    Article  Google Scholar 

  12. Hare, S., Saffari, A., Torr, P.H.: Struck: Structured output tracking with kernels. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 263–270 (2011)

    Google Scholar 

  13. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012)

    Article  Google Scholar 

  14. Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Part-based visual tracking with online latent structural learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2363–2370 (2013)

    Google Scholar 

  15. Yang, M., Pei, M., Wu, Y., Jia, Y.: Learning online structural appearance model for robust object tracking. Sci. China Inf. Sci. 58(3), 1–14 (2015)

    Article  Google Scholar 

  16. Yu, Qian, Dinh, Thang Ba, Medioni, Gérard G.: Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Forsyth, David, Torr, Philip, Zisserman, Andrew (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 678–691. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Zhang, L., van der Maaten, L.: Preserving structure in model-free tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36, 756–769 (2014)

    Article  Google Scholar 

  18. Wu, Y., Ma, B., Yang, M., Jia, Y., Zhang, J.: Metric learning based structural appearance model for robust visual tracking. IEEE Trans. Circuits Syst. Video Technol. 24, 865–877 (2014)

    Article  Google Scholar 

  19. Gao, J., Xing, J., Hu, W., Maybank, S.: Discriminant tracking using tensor representation with semi-supervised improvement. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1569–1576 (2013)

    Google Scholar 

  20. Li, X., Shen, C., Dick, A.R., van den Hengel, A.: Learning compact binary codes for visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2419–2426 (2013)

    Google Scholar 

  21. Kumar, K., Vleeschouwer, C.: Discriminative label propagation for multi-object tracking with sporadic appearance features. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2000–2007 (2013)

    Google Scholar 

  22. Zhang, K., Kwok, J.T., Parvin, B.: Prototype vector machine for large scale semi-supervised learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1233–1240. ACM (2009)

    Google Scholar 

  23. Liu, W., Wang, J., Chang, S.F.: Robust and scalable graph-based semisupervised learning. Proc. IEEE 100, 2624–2638 (2012)

    Article  Google Scholar 

  24. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3360–3367 IEEE (2010)

    Google Scholar 

  25. Nesterov, Y.: Introductory Lectures on Convex Optimization: A Basic Course. Kluwer Academic Publishers, Dordrecht (2004)

    Book  Google Scholar 

  26. Duchi, J., Shalev-Shwartz, S., Singer, Y., Chandra, T.: Efficient projections onto the l1-ball for learning in high dimensions. In: Proceedings of the 25th international conference on Machine learning, pp. 272–279. ACM (2008)

    Google Scholar 

  27. Williams, C., Seeger, M.: Using the nyström method to speed up kernel machines. In: Advances in Neural Information Processing Systems, Citeseer (2001)

    Google Scholar 

  28. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418. IEEE (2013)

    Google Scholar 

  29. Wang, N., Wang, J., Yeung, D.Y.: Online robust non-negative dictionary learning for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 657–664 (2013)

    Google Scholar 

  30. Bai, Q., Wu, Z., Sclaroff, S., Betke, M., Monnier, C.: Randomized ensemble tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2040–2047 (2013)

    Google Scholar 

  31. 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. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  32. Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparsity-based collaborative model. IEEE Trans. Image Process. 23, 2356–2368 (2014)

    Article  MathSciNet  Google Scholar 

  33. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, vol. 2. Springer, New York (2009)

    Book  MATH  Google Scholar 

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Acknowledgement

This work was supported in part by the Natural Science Foundation of China (NSFC) under grant No. 61203291, the 973 Program of China under grant No. 2012CB720000, the Specialized Research Fund for the Doctoral Program of Higher Education of China under grant No.20121101120029, and the Specialized Fund for Joint Building Program of Beijing Municipal Education Commission.

Author information

Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 10081, People’s Republic of China

    Yuwei Wu, Mingtao Pei, Min Yang, Yang He & Yunde Jia

Authors
  1. Yuwei Wu
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  2. Mingtao Pei
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  3. Min Yang
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  4. Yang He
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  5. Yunde Jia
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Corresponding author

Correspondence to Yuwei Wu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Wu, Y., Pei, M., Yang, M., He, Y., Jia, Y. (2015). Landmark-Based Inductive Model for Robust Discriminative Tracking. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

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