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Frontiers of Computer Science

, Volume 12, Issue 6, pp 1160–1172 | Cite as

Robust feature learning for online discriminative tracking without large-scale pre-training

  • Jun Zhang
  • Bineng ZhongEmail author
  • Pengfei Wang
  • Cheng Wang
  • Jixiang Du
Research Article
  • 19 Downloads

Abstract

Owing to the inherent lack of training data in visual tracking, recent work in deep learning-based trackers has focused on learning a generic representation offline from large-scale training data and transferring the pre-trained feature representation to a tracking task. Offline pre-training is time-consuming, and the learned generic representation may be either less discriminative for tracking specific objects or overfitted to typical tracking datasets. In this paper, we propose an online discriminative tracking method based on robust feature learning without large-scale pre-training. Specifically, we first design a PCA filter bank-based convolutional neural network (CNN) architecture to learn robust features online with a few positive and negative samples in the high-dimensional feature space. Then, we use a simple soft-thresholding method to produce sparse features that are more robust to target appearance variations. Moreover, we increase the reliability of our tracker using edge information generated from edge box proposals during the process of visual tracking. Finally, effective visual tracking results are achieved by systematically combining the tracking information and edge box-based scores in a particle filtering framework. Extensive results on the widely used online tracking benchmark (OTB-50) with 50 videos validate the robustness and effectiveness of the proposed tracker without large-scale pre-training.

Keywords

visual tracking convolutional neural networks PCA Edge Box 

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Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61572205 and 61175121), Natural Science Foundation of Fujian Province (2015J01257), Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (ZQN-PY210 and ZQN-YX108), 2015 Program for New Century Excellent Talents in Fujian Province University, Project of science and technology plan of Fujian Province of China (2017H01010065).

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References

  1. 1.
    Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564–577CrossRefGoogle Scholar
  2. 2.
    Danelljan M, Khan F S, Felsberg M, Weijer J V D. Adaptive color attributes for real-time visual tracking. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2014, 1090–1097Google Scholar
  3. 3.
    Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008, 77(1–3): 125–141CrossRefGoogle Scholar
  4. 4.
    Wang Q, Chen F, Xu W L, Yang M H. Object tracking via partial least squares analysis. IEEE Transactions on Image Processing, 2012, 21(10): 4454–4465MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137–154CrossRefGoogle Scholar
  6. 6.
    Grabner H, Bischof H. On-line boosting and vision. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2006, 260–267Google Scholar
  7. 7.
    Hare S, Saffari A, Torr P. Struck: structured output tracking with kernels. IEEE International Conference on Computer Vision and Pattern Recognition. 2011Google Scholar
  8. 8.
    Yao R, Shi Q F, Shen C H, Zhang Y N, Hengel A V D. Part-based visual tracking with online latent structural learning. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2013, 2363–2370Google Scholar
  9. 9.
    Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037–2041CrossRefzbMATHGoogle Scholar
  10. 10.
    Takala V, Pietikainen M. Multi-object tracking using color, texture and motion. In: Proceedings of IEEE Conference on Computer Vission and Pattern Recognition, 2007CrossRefGoogle Scholar
  11. 11.
    Yang F, Lu H, Zhang W, Yang G. Visual tracking via bag of features. IEEE Transactions on Image Processing, 2012, 6(2): 115–128MathSciNetCrossRefGoogle Scholar
  12. 12.
    Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 886–893Google Scholar
  13. 13.
    Godec M, Roth P M, Bischof H. Hough-based tracking of non-rigid objects. Computer Vision and Image Understanding, 2011, 117(10): 1245–1256CrossRefGoogle Scholar
  14. 14.
    Lu Y, Wu T F, Zhu S C. Online object tracking, learning and parsing with and-or graphs. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2014, 3462–3469Google Scholar
  15. 15.
    Grabner H, Matas J, Gool L V, Cattin P. Tracking the invisible: learning where the object might be. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2010Google Scholar
  16. 16.
    Fan J L, Shen X H, Wu Y. Scribble tracker: a matting-based approach for robust tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(8): 1633–1634CrossRefGoogle Scholar
  17. 17.
    Porikli F, Tuzel O, Meer P. Covariance tracking using model update based on lie algebra. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2006, 728–735Google Scholar
  18. 18.
    Wu Y, Cheng J, Wang J, Lu H, Wang J, Ling H, Blasch E, Bai L. Real-time probabilistic covariance tracking with efficient model update. IEEE Transactions on Image Processing, 2012, 21(5): 2824–2837MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Li X, Dick A, Shen C H, Hengel A V D, Wang H Z. Incremental learning of 3D-DCT compact representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4): 863–881CrossRefGoogle Scholar
  20. 20.
    Isard M, Blake A. CONDENSATION—conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1): 5–28CrossRefGoogle Scholar
  21. 21.
    Wang S, Lu H, Yang F, Yang MH. Superpixel tracking. In: Proceedings of International Conference on Computer Vision. 2011, 1323–1330Google Scholar
  22. 22.
    Smeulders A W M, Chu D M, Cucchiara R, Calderara S, Dehghan A, Shah M. Visual tracking: an experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442–1468CrossRefGoogle Scholar
  23. 23.
    Li X, Hu W, Shen C, Zhang Z, Dick A, van den Hengel A. A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 1–42CrossRefGoogle Scholar
  24. 24.
    Collins R T, Liu Y, Leordeanu M. Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631–1643CrossRefGoogle Scholar
  25. 25.
    Mei X, Ling H. Robust visual tracking using L1 minimization. In: Proceedings of International Conference on Computer Vision. 2009, 1436–1443Google Scholar
  26. 26.
    Bao C, Wu Y, Ling H, Ji H. Real time robust L1 tracker using accelerated proximal gradient approach. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2012, 1830–1837Google Scholar
  27. 27.
    Zhang K H, Zhang L, Yang M H. Real-time compressive tracking. In: Proceedings of European Conference on Compute Vision. 2012, 864–877Google Scholar
  28. 28.
    Zhang T, Ghanem B, Liu S, Ahuja N. Low-rank sparse learning for robust visual tracking. In: Proceedings of European Conference on Compute Vision. 2012, 470–484Google Scholar
  29. 29.
    Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829Google Scholar
  30. 30.
    Zhang Z, Wong K H. Pyramid-based visual tracking using sparsity represented mean transform. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2014, 1226–1233Google Scholar
  31. 31.
    Zhong B N, Yao H X, Chen S, Ji R R, Chin T J, Wang H Z. Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition, 2014, 47(3): 1395–1410CrossRefzbMATHGoogle Scholar
  32. 32.
    Hong Z, Chen Z, Wang C, Mei X, Prokhorov D, Tao D. Multistore tracker (muster): a cognitive psychology inspired approach to object tracking. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2015, 749–758Google Scholar
  33. 33.
    Bai Y, Tang M. Robust tracking via weakly supervised ranking SVM. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2012, 1854–1861Google Scholar
  34. 34.
    Zuo W M, Wu X H, Lin L, Zhang L, Yang M H. Learning support correlation filters for visual tracking. 2016, arXiv:1601.06032Google Scholar
  35. 35.
    Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409–1422CrossRefGoogle Scholar
  36. 36.
    Babenko B, Yang M, Belongie S. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619–1632CrossRefGoogle Scholar
  37. 37.
    Santner J, Leistner C, Saffari A, Pock T, Bischof H. PROST: parallel robust online simple tracking. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2010, 723–730Google Scholar
  38. 38.
    Gall J, Yao A, Van L, Lempitsky V. Hough forests for object detection, tracking, and action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2188–2202CrossRefGoogle Scholar
  39. 39.
    Zhang L, Maaten L V D. Preserving structure in model-free tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(4): 756–769CrossRefGoogle Scholar
  40. 40.
    Duffner S, Garcia C. Pixeltrack: a fast adaptive algorithm for tracking non-rigid objects. International Conference on Computer Vision. 2013, 2480–2487Google Scholar
  41. 41.
    Cehovin L, Kristan M, Leonardis A. Robust visual tracking using an adaptive coupled-layer visual model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4): 941–953CrossRefGoogle Scholar
  42. 42.
    Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583–596CrossRefGoogle Scholar
  43. 43.
    Chen Z, Hong Z B, Tao D C. An experimental survey on correlation filter-based tracking. 2015, arXiv:1509.05520Google Scholar
  44. 44.
    Liang P P, Liao C Y, Mei X, Ling H B. Adaptive objectness for object tracking. 2015, arXiv:1501.00909Google Scholar
  45. 45.
    Cheng M M, Zhang Z M, Lin W Y, Torr P. BING: binarized normed gradients for objectness estimation at 300fps. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2014, 3286–3293Google Scholar
  46. 46.
    Hua Y, Alahari K, Schmid C. Online object tracking with proposal selection. In: Proceedings of International Conference on Computer Vision. 2015, 3092–3100.Google Scholar
  47. 47.
    Zhu G, Porikli F, Li H D. Tracking randomly moving objects on Edge Box proposals. 2015, arXiv:1507.08085Google Scholar
  48. 48.
    Gan Y, Liu J, Dong J Y, Zhong G Q. A PCA-based convolutional network. 2015, arXiv:1505.03703Google Scholar
  49. 49.
    Guo Y W, Chen Y, Tang F, Li A, Luo W T, Liu M M. Object tracking using learned feature manifolds. Computer Vision and Image Understanding, 2014, 118: 128–139CrossRefGoogle Scholar
  50. 50.
    Fan J L, Xu W, Wu Y, Gong Y H. Human tracking using convolutional neural networks. TEEE Transactions on Neural Networks, 2010, 21(10): 1610–1623CrossRefGoogle Scholar
  51. 51.
    Wang N Y, Yeung D Y. Learning a deep compact image representation for visual tracking. In: Proceedings of Neural Information Processing Systems Conference. 2013, 809–817Google Scholar
  52. 52.
    Wang L, Liu T, Wang G, Chan K L, Yang Q. Video tracking using learned hierarchical features. IEEE Transactions on Image Processing, 2015, 24(4): 1424–1435MathSciNetCrossRefGoogle Scholar
  53. 53.
    Li H X, Li Y, Porikli F. Deeptrack: learning discriminative feature representations by convolutional neural networks for visual tracking. In: Proceedings of British Machine Vision Conference. 2014Google Scholar
  54. 54.
    Wang L J, Ouyang WL,Wang X G, Lu H C. Visual tracking with fully convolutional networks. In: Proceedings of International Conference on Computer Vision, 2015, 3119–3127Google Scholar
  55. 55.
    Hong S, You T, Kwak S, Han B. Online tracking by learning discriminative saliency map with convolutional neural network. In: Proceedings of International Conference on Machine Learning. 2015, 597–606Google Scholar
  56. 56.
    Ma C, Huang J B, Yang X K, Yang M H. Hierarchical convolutional features for visual tracking. In: Proceedings of International Conference on Computer Vision. 2015, 3074–3082Google Scholar
  57. 57.
    Nam H S, Han B Y. Learning multi-domain convolutional neural networks for visual tracking. 2015, arXiv:1510.07945Google Scholar
  58. 58.
    Elad M, Figueiredo M A, Ma Y. On the role of sparse and redundant representations in image processing. Proceedings of the IEEE, 2010, 98(6): 972–982CrossRefGoogle Scholar
  59. 59.
    Wu Y, Lim J W, Yang M H. Online object tracking: a benchmark. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. 2013, 2411–2418Google Scholar
  60. 60.
    Yilmaz A, Javed O. Shah M. Object tracking: a survey. ACM Computing Surveys, 2006, 38(4):13.CrossRefGoogle Scholar
  61. 61.
    Dollár P, Zitnick C T. Structured forests for fast edge detection. In: Proceedings of International Conference on Computer Vision. 2013, 1841–1848Google Scholar
  62. 62.
    Zitnick C L, Doll’ar P. Edge boxes: locating object proposals from edges. In: Proceedings of European Conference on Compute Vision. 2014, 391–405Google Scholar
  63. 63.
    Zhang J M, Ma S G, Sclaroff S. MEEM: robust tracking via multiple experts using entropy minimization. In: Proceedings of European Conference on Compute Vision. 2014Google Scholar
  64. 64.
    Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583–596CrossRefGoogle Scholar
  65. 65.
    Gao J, Ling H, Hu W, Xing J. Transfer learning based visual tracking with gaussian processes regression. In: Proceedings of European Conference on Compute Vision. 2014Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhang
    • 1
  • Bineng Zhong
    • 1
    Email author
  • Pengfei Wang
    • 1
  • Cheng Wang
    • 1
  • Jixiang Du
    • 1
  1. 1.Department of Computer Science and TechnologyHuaqiao UniversityQuanzhouChina

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