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Distributed Video Sensor Networks pp 119–136Cite as

Toward Robust Online Visual Tracking

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Abstract

We pursue a research direction that will empower machines with simultaneous tracking and recognition capabilities similar to human cognition. Toward that, we develop algorithms that leverage prior knowledge/model obtained offline with information available online via novel learning algorithms. While humans can effortlessly locate moving objects in different environments, visual tracking remains one of the most important and challenging problems in computer vision. Robust cognitive visual tracking algorithms facilitate answering important questions regarding how objects move and interact in complex environments. They have broad applications including surveillance, navigation, human computer interfaces, object recognition, motion analysis and video indexing, to name a few.

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References

  1. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 798–805 (2006)

    Google Scholar 

  2. Adelson, E.H., Bergen, J.R.: The plenoptic function and the elements of early vision. In: Landy, M., Movshon, J.A. (eds.) Computational Models of Visual Processing, pp. 1–20. MIT Press, Cambridge (1991)

    Google Scholar 

  3. Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)

    Article  Google Scholar 

  4. Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 983–990 (2009)

    Google Scholar 

  5. Balan, A.O., Black, M.J.: An adaptive appearance model approach for model-based articulated object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 758–765 (2006)

    Google Scholar 

  6. Belhumeur, P., Kreigman, D.: What is the set of images of an object under all possible lighting conditions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 270–277 (1997)

    Google Scholar 

  7. Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 26(1), 63–84 (1998)

    Article  Google Scholar 

  8. Bregler, C., Malik, J.: Tracking people with twists and exponential map. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8–15 (1998)

    Google Scholar 

  9. Cham, T.J., Rehg, J.M.: A multiple hypothesis approach to figure tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 239–245 (1998)

    Google Scholar 

  10. Charikar, M., Chekuri, C., Feder, T., Motwani, R.: Incremental clustering and dynamic information retrieval. SIAM J. Comput. 33(6), 1417–1440 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  11. Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005).

    Article  Google Scholar 

  12. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)

    Article  Google Scholar 

  13. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  14. Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 126–133 (2000)

    Google Scholar 

  15. Dietterich, T.G., Lathrop, R.H., Perez, L.T.: Solving the multiple-instance problem with axis parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)

    Article  MATH  Google Scholar 

  16. Dollár, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007

    Google Scholar 

  17. Forsyth, D., Arikan, O., Ikemoto, L., O’Brien, J., Ramanan, D.: Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis. Now publishers, Hanover (2006)

    Google Scholar 

  18. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  19. Friedman, J.H.: Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MATH  Google Scholar 

  20. Ghahramani, Z., Beal, M.: Variational inference for Bayesian mixtures of factor analysers. In: Advances in Neural Information Processing Systems, pp. 449–455 (2000)

    Google Scholar 

  21. Golub, G.H., Van Loan, C.F.: Matrix Computations. The Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  22. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: Proceedings of British Machine Vision Conference, pp. 47–56 (2006)

    Google Scholar 

  23. Hager, G.D., Belhumeur, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Anal. Mach. Intell. 20(10), 1025–1039 (1998)

    Article  Google Scholar 

  24. Hall, P., Marshall, D., Martin, R.: Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image Vis. Comput. 20(13–14), 1009–1016 (2002)

    Article  Google Scholar 

  25. Ho, J., Lee, K.-C., Yang, M.-H., Kriegman, D.: Visual tracking using learned linear subspaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 782–789 (2004)

    Google Scholar 

  26. Humphreys, G., Bruce, V.: Visual Cognition: Computational, Experimental and Neuropsychological Perspectives. Psychology Press, London (1989)

    MATH  Google Scholar 

  27. Ioffe, S., Forsyth, D.: Probabilistic methods for finding people. Int. J. Comput. Vis. 43(1), 45–68 (2001)

    Article  MATH  Google Scholar 

  28. Isard, M., Blake, A.: CONDENSATION—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  29. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  30. Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1296–1311 (2003)

    Article  Google Scholar 

  31. Lee, K.-C., Ho, J., Yang, M.-H., Kriegman, D.: Visual tracking and recognition using probabilistic appearance manifolds. Comput. Vis. Image Underst. 99(3), 303–331 (2005)

    Article  Google Scholar 

  32. Levy, A., Lindenbaum, M.: Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)

    Article  MATH  Google Scholar 

  33. Li, R., Yang, M.-H., Sclaroff, S., Tian, T.-P.: Monocular tracking of 3D human motion with a coordinated mixture of factor analyzers. In: Proceedings of European Conference on Computer Vision, pp. 137–150 (2006)

    Google Scholar 

  34. Li, R., Tian, T.-P., Sclaroff, S., Yang, M.-H.: 3D human motion tracking with a coordinated mixture of factor analyzers. Int. J. Comput. Vis. 87(1–2), 170–190 (2010)

    Article  Google Scholar 

  35. Lim, J., Ross, D., Lin, R.-S., Yang, M.-H.: Incremental learning for visual tracking. In: Advances in Neural Information Processing Systems, pp. 793–800. MIT Press, Cambridge (2005)

    Google Scholar 

  36. Lin, R.-S., Liu, C.-B., Yang, M.-H., Ahuja, N., Levinson, S.: Learning nonlinear manifolds from time series. In: Proceedings of European Conference on Computer Vision, pp. 239–250 (2004)

    Google Scholar 

  37. Lin, R.-S., Ross, D., Lim, J., Yang, M.-H.: Adaptive discriminative generative model and its applications. In: Advances in Neural Information Processing Systems, pp. 801–808. MIT Press, Cambridge (2005)

    Google Scholar 

  38. Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23(4), 349–361 (2001)

    Article  Google Scholar 

  39. Moselund, T., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)

    Article  Google Scholar 

  40. Murase, H., Nayar, S.: Visual learning and recognition of 3d objects from appearance. Int. J. Comput. Vis. 14(1), 5–24 (1995)

    Article  Google Scholar 

  41. Nejhum, S.M.S., Ho, J., Yang, M.-H.: Online articulate object tracking with appearance and shape. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008

    Google Scholar 

  42. Oza, N.C.: Online Ensemble Learning. Ph.D. Thesis, University of California, Berkeley (2001)

    Google Scholar 

  43. Pentland, A., Moghaddam, B., Starner, T., Oligide, O., Turk, M.: View-based and modular eigenspaces for face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 84–91 (1994)

    Chapter  Google Scholar 

  44. Porikli, F.: Integral histogram: A fast way to extract histograms in Cartesian spaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 829–836 (2005)

    Google Scholar 

  45. Ramanan, D., Forsyth, D.: Finding and tracking people from the bottom up. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 467–474 (2003)

    Google Scholar 

  46. Ronfard, R., Schmid, C., Triggs, B.: Learning to parse pictures of people. In: Proceedings of the Seventh European Conference on Computer Vision, pp. 700–714 (2002)

    Google Scholar 

  47. Ross, D., Lim, J., Yang, M.-H.: Adaptive probabilistic visual tracking with incremental subspace update. In: Proceedings of European Conference on Computer Vision, pp. 470–482 (2004)

    Google Scholar 

  48. Ross, D., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  49. Roweis, S., Saul, L., Hinton, G.E.: Global coordination of local linear models. In: Advances in Neural Information Processing Systems, pp. 889–896 (2001)

    Google Scholar 

  50. Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998)

    Article  Google Scholar 

  51. Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. Int. J. Comput. Vis. 56(3), 151–177 (2004)

    Article  Google Scholar 

  52. Sidenbladh, H., Black, M.: Learning image statistics for Bayesian tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 709–716 (2001)

    Google Scholar 

  53. Sigal, L., Bhatia, S., Roth, S., Black, M., Isard, M.: Tracking loose-limbed people. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 421–428 (2004)

    Google Scholar 

  54. Sigal, L., Black, M.: HumanEva: synchronized video and motion capture dataset for evaluation of articulated human motion. Technical Report CS-06-08, Brown University (2006)

    Google Scholar 

  55. Sminchisescu, C., Triggs, B.: Covariance scaled sampling for monocular 3D body tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 447–454 (2001)

    Google Scholar 

  56. Sung, K.-K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)

    Article  Google Scholar 

  57. Teh, Y.W., Roweis, S.: Automatic alignment of local representations. In: Advances in Neural Information Processing Systems, pp. 841–848 (2002)

    Google Scholar 

  58. Tian, T.-P., Li, R., Sclaroff, S.: Tracking human body pose on a learned smooth space. Technical Report 2005-029, Boston University (2005)

    Google Scholar 

  59. Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. Int. J. Comput. Vis. 48(1), 9–19 (2002)

    Article  MATH  Google Scholar 

  60. Urtasun, R., Fleet, D., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: Proceedings of IEEE International Conference on Computer Vision, pp. 403–410 (2005)

    Google Scholar 

  61. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)

    Google Scholar 

  62. Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: Advances in Neural Information Processing Systems, pp. 1417–1426. MIT Press, Cambridge (2005)

    Google Scholar 

  63. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38(4), 1–45 (2006)

    Article  Google Scholar 

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Authors and Affiliations

    Authors
    1. Ming-Hsuan Yang
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    2. Jeffrey Ho
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    Corresponding author

    Correspondence to Ming-Hsuan Yang .

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    Editors and Affiliations

    1. Center for Research, Intelligent Systems, University of California, Riverside, Riverside, 92521, California, USA

      Bir Bhanu

    2. Dept. Computer Science & Engineering, University of California, Riverside, Riverside, 92521, California, USA

      Chinya V. Ravishankar

    3. Dept. Electrical Engineering, University of California, Riverside, Riverside, 92521, California, USA

      Amit K. Roy-Chowdhury

    4. Dept. Electrical Engineering, Packard Bldg., Stanford University, Serra Mall 350, Stanford, 94305-9505, California, USA

      Hamid Aghajan

    5. Dept. Computer Science, University of California, Los Angeles, Boelter Hall 4731, Los Angeles, 90095-1596, California, USA

      Demetri Terzopoulos

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    © 2011 Springer-Verlag London Limited

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    Yang, MH., Ho, J. (2011). Toward Robust Online Visual Tracking. In: Bhanu, B., Ravishankar, C., Roy-Chowdhury, A., Aghajan, H., Terzopoulos, D. (eds) Distributed Video Sensor Networks. Springer, London. https://doi.org/10.1007/978-0-85729-127-1_8

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    • DOI: https://doi.org/10.1007/978-0-85729-127-1_8

    • Publisher Name: Springer, London

    • Print ISBN: 978-0-85729-126-4

    • Online ISBN: 978-0-85729-127-1

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