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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 146–161Cite as

Visual Tracking via Supervised Similarity Matching

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

Abstract

Supervised learning algorithms have been widely applied in tracking-by-detection based methods for object tracking in recent years. Most of these approaches treat tracking as a classification problem and solve it by training a discriminative classifier and exhaustively evaluating every possible target position; problems thus exist for two reasons. First, since the classifier describes the common feature of samples in an implicit way, it is not clear how well the classifier can represent the feature of the desired object against others; second, the brute-force search within the output space is usually time consuming, and thus limits the competence for real-time application. In this paper, we treat object tracking as a problem of similarity matching for streaming data. We propose to apply unsupervised learning by Locality Sensitive Hashing (LSH) and use LSH based similarity matching as the main engine for target detection. In addition, our method applies a Support Vector Machine (SVM) based supervised classifier cooperating with the unsupervised detector. Both the proposed tracker and several selected trackers are tested on some well accepted challenging videos; and the experimental results demonstrate that the proposed tracker outperforms the selected other trackers in terms of the effectiveness as well as the robustness.

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References

  1. Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1064–1072 (2004)

    Article  Google Scholar 

  2. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via online boosting. In: BMVC, pp. 47–56 (2006)

    Google Scholar 

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

    Chapter  Google Scholar 

  4. Stalder, S., Grabner, H., Gool., L.V.: Beyond semi-supervised tracking: tracking should be as simple as detection, but not simpler than recognition. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1409–1416 (2009)

    Google Scholar 

  5. Babenko, B., Yang, M.H., Belongie., S.: Visual tracking with online multiple instance learning. In: Computer Vision and Pattern Recognition 2009, pp. 983–990 (2009)

    Google Scholar 

  6. Hare, S., Saffari, A., Torr., P.H.: Struck: structured output tracking with kernels. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 263–270 (2011)

    Google Scholar 

  7. Bordes, A., Bottou, L., Gallinari, P., Weston., J.: Solving multiclass support vector machines with larank. In: Proceedings of the 24th International Conference on Machine Learning, pp. 89–96 (2007)

    Google Scholar 

  8. Bordes, A., Usunier, N., Bottou, L.: Sequence labelling SVMs trained in one pass. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 146–161. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Crammer, K., Kandola, J.S., Singer., Y.: Online classification on a budget. In: Neural Information Processing Systems (NIPS), vol. 2, 5 (2003)

    Google Scholar 

  10. Wang, Z., Crammer, K., Vucetic., S.: Multi-class pegasos on a budget. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), vol. 2, pp. 1143–1150 (2010)

    Google Scholar 

  11. Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Robust tracking with weighted online structured learning. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 158–172. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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

    Google Scholar 

  13. Li, X., Shen, C., Dick, A., van den Hengel., A.: Learning compact binary codes for visual tracking. In: Computer Vision and Pattern Recognition (CVPR) 2013, pp. 2419–2426 (2013)

    Google Scholar 

  14. Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof., H.: On-line random forests. In: Computer Vision Workshops (ICCV Workshops) 2009, pp. 1393–1400 (2009)

    Google Scholar 

  15. 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 

  16. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 864–877 (2012)

    Article  Google Scholar 

  17. Schlkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt., J.C.: Support vector method for novelty detection. In: Neural Information Processing Systems(NIPS), vol. 12, pp. 582–588 (1999)

    Google Scholar 

  18. Gmez-Verdejo, V., Arenas-Garca, J., Lazaro-Gredilla, M., Navia-Vazquez, A.: Adaptive one-class support vector machine. IEEE Trans. Signal Process. 59, 2975–2981 (2011)

    Article  MathSciNet  Google Scholar 

  19. Indyk, P., Motwani., R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)

    Google Scholar 

  20. Datar, M., Immorlica, N., Indyk, P., Mirrokni., V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262 (2004)

    Google Scholar 

  21. Weiss, Y., Torralba, A., Fergus., R.: Spectral hashing. In: Neural Information Processing Systems (NIPS), vol. 9, 6 (2008)

    Google Scholar 

  22. Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li., K.: Multi-probe LSH: efficient indexing for high-dimensional similarity search. In: Proceedings of the 33rd International Conference on Very large Data Bases, pp. 950–961 (2007)

    Google Scholar 

  23. Kulis, B., Grauman., K.: Kernelized locality-sensitive hashing for scalable image search. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2130–2137 (2009)

    Google Scholar 

  24. Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon., S.E.: Spherical hashing. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2957–2964 (2012)

    Google Scholar 

  25. Zhang, T., Ghanem, B., Liu, S., Ahuja., N.: Robust visual tracking via multi-task sparse learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2042–2049 (2012)

    Google Scholar 

  26. Adam, A., Rivlin, E., Shimshoni., I.: Robust fragments-based tracking using the integral histogram. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.798–805 (2006)

    Google Scholar 

  27. Sevilla-Lara, L., Learned-Miller., E.: Distribution fields for tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1910–1917 (2012)

    Google Scholar 

  28. Bao, C., Wu, Y., Ling, H., Ji., H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1830–1837 (2012)

    Google Scholar 

  29. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision (IJCV) 77, 125–141 (2012)

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  32. He, S., Yang, Q., Lau, R.W., Wang, J., Yang., M.H.: Visual tracking via locality sensitive histograms. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2427–2434 (2013)

    Google Scholar 

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

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Data Science, Institute of Technology, University of Washington Tacoma, Tacoma, USA

    Ji Zhang, Jie Sheng & Ankur Teredesai

Authors
  1. Ji Zhang
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  2. Jie Sheng
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  3. Ankur Teredesai
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Corresponding author

Correspondence to Ji Zhang .

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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© 2015 Springer International Publishing Switzerland

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Zhang, J., Sheng, J., Teredesai, A. (2015). Visual Tracking via Supervised Similarity Matching. 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_10

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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