Frontiers of Computer Science

, Volume 11, Issue 2, pp 230–242 | Cite as

Robust visual tracking based on scale invariance and deep learning

  • Nan Ren
  • Junping Du
  • Suguo Zhu
  • Linghui Li
  • Dan Fan
  • JangMyung Lee
Research Article

Abstract

Visual tracking is a popular research area in computer vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rotation, fast motion, and occlusion. Consequently, the focus in this research area is to make tracking algorithms adapt to these changes, so as to implement stable and accurate visual tracking. This paper proposes a visual tracking algorithm that integrates the scale invariance of SURF feature with deep learning to enhance the tracking robustness when the size of the object to be tracked changes significantly. Particle filter is used for motion estimation. The confidence of each particle is computed via a deep neural network, and the result of particle filter is verified and corrected by mean shift because of its computational efficiency and insensitivity to external interference. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods throughout the challenging factors in visual tracking, especially for scale variation.

Keywords

visual tracking SURF mean shift particle filter neural network 

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Supplementary material

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References

  1. 1.
    Jia Y M. Robust control with decoupling performance for steering and traction of 4WS vehicles under velocity-varying motion. IEEE Transactions on Control Systems Technology, 2000, 8(3): 554–569CrossRefGoogle Scholar
  2. 2.
    Jia Y M. Alternative proofs for improved LMI representations for the analysis and the design of continuous-time systems with polytopic type uncertainty: a predictive approach. IEEE Transactions on Automatic Control, 2003, 48(8): 1413–1416MathSciNetCrossRefGoogle Scholar
  3. 3.
    Jia Y M. General solution to diagonal model matching control of multiple-output-delay systems and its applications in adaptive scheme. Progress in Natural Science, 2009, 19(1): 79–90MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wang N Y, Yeung D Y. Learning a deep compact image representation for visual tracking. In: Proceedings of Advances in Neural Information Processing Systems. 2013, 809–817Google Scholar
  5. 5.
    Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 2010, 11: 3371–3408MathSciNetMATHGoogle Scholar
  6. 6.
    Smeulders AWM, Chu DM, Rita C, Simone C, Afshin D, Mubarak S. Visual tracking: an experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442–1468CrossRefGoogle Scholar
  7. 7.
    Ali A, Jalil A, Niu J, Zhao X K, Rathore S, Ahmed J, Iftikhar M A. Visual object tracking—classical and contemporary approaches. Frontiers of Computer Science, 2016, 10(1): 167–188CrossRefGoogle Scholar
  8. 8.
    Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. In: proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 9(4): 2411–2418Google Scholar
  9. 9.
    Wu Y, Lim J, Yang M H. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848CrossRefGoogle Scholar
  10. 10.
    Li X, Dick A, Shen C H, Anton V D H, 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
  11. 11.
    Gao J, Ling H B, Hu W M, Xing J L. Transfer learning based visual tracking with Gaussian processes regression. In: Proceedings of the 13th European Conference on Computer Vision. 2014, 188–203Google Scholar
  12. 12.
    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
  13. 13.
    Li X, Shen C H, Dick A, Zhang Z M, Zhuang Y. Online metricweighted linear representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 931–950CrossRefGoogle Scholar
  14. 14.
    Zhou Y, Bai X, Liu WY, Latecki L J. Similarity fusion for visual tracking. International Journal of Computer Vision, 2016, 118(3): 337–363MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zhong W, Lu H C, Yang M H. Robust object tracking via sparsitybased collaborative model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1838–1845Google Scholar
  16. 16.
    Hare S, Saffari A, Torr P H S. Struck: structured output tracking with kernels. In: proceedings of IEEE Conference on Computer Vision. 2011, 263–270Google Scholar
  17. 17.
    Li X, Dick A, Shen C H, Zhang Z F, Hengel A V D, Wang H Z. Visual tracking with spatio-temporal Dempster-Shafer information fusion. IEEE Transactions on Image Processing, 2013, 22(8): 3028–3040MathSciNetCrossRefGoogle Scholar
  18. 18.
    Gao C X, Chen F F, Yu J G, Huang R, Sang N. Robust visual tracking using exemplar-based detectors. IEEE Transactions on Circuits and Systems for Video Technology, 2015Google Scholar
  19. 19.
    Li K, He F Z, Chen X. Real-time object tracking via compressive feature selection. Frontiers of Computer Science, 2016, 10(4): 689–701CrossRefGoogle Scholar
  20. 20.
    Zhang B C, Perina A, Li Z G, Murino V, Liu J Z, Ji R R. Bounding multiple gaussians uncertainty with application to object tracking. International Journal of Computer Vision, 2016, 118(3): 364–379MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zhu Y Y, Zhang C Q, Zhou D Y, Wang X G, Bai X, Liu W Y. Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing, 2016, 214: 758–766CrossRefGoogle Scholar
  22. 22.
    Li H X, Li Y, Porikli F. Deep Track: learning discriminative feature representations by convolutional neural networks for visual tracking. IEEE Transactions on Image Processing, 2015, 25(4): 1834–1848CrossRefGoogle Scholar
  23. 23.
    Hong S H, You T G, Kwak S H, Han B H. Online tracking by learning discriminative saliency map with convolutional neural network. 2015, arXiv:1502.06796v1Google Scholar
  24. 24.
    Wang L, Liu T, Wang G, Chan K L, Yang Q X. Video tracking using learned hierarchical features. IEEE Transactions on Image Processing, 2015, 24(4): 1424–1435MathSciNetCrossRefGoogle Scholar
  25. 25.
    Ma C, Huang J B, Yang X K, Yang M H. Hierarchical convolutional features for visual tracking. In: proceedings of IEEE International Conference on Computer Vision. 2015, 3074–3082Google Scholar
  26. 26.
    Wang N Y, Li S Y, Gupta A, Yeung D Y. Transferring rich feature hierarchies for robust visual tracking. 2015, arXiv:1501.04587v2Google Scholar
  27. 27.
    Zhang K H, Liu Q S, Wu Y, Yang M H. Robust visual tracking via convolutional networks without training. IEEE Transactions on Image Processing, 2016, 25(4): 1779–1792MathSciNetGoogle Scholar
  28. 28.
    Held D, Thrun S, Savarese S. Learning to track at 100 fps with deep regression networks. 2016, arXiv:1604.01802CrossRefGoogle Scholar
  29. 29.
    Wang L J, Ouyang W L, Wang X G, Lu H C. STCT: sequentially training convolutional networks for visual tracking. In: proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016Google Scholar
  30. 30.
    Zhai MY, Roshtkhari M J, Mori G. Deep Learning of appearance models for online object tracking. 2016, arXiv:1607.02568Google Scholar
  31. 31.
    Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188CrossRefGoogle Scholar
  32. 32.
    Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564–577CrossRefGoogle Scholar
  33. 33.
    Torralba A, Fergus R, Freeman W T. 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1958–1970CrossRefGoogle Scholar
  34. 34.
    Zhang J M, Ma S G, Sclaroff S. MEEM: robust tracking via multiple experts using entropy minimization. In: proceedings of European Conference on Computer Vision. 2014, 188–203Google Scholar
  35. 35.
    He S F, Yang Q X, Lau RWH, Wang J, Yang M H. Visual tracking via locality sensitive histograms. In: proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2427–2434Google Scholar
  36. 36.
    Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829Google Scholar
  37. 37.
    Kwon J, Lee K M. Visual tracking decomposition. In: proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276Google Scholar
  38. 38.
    Ross D A, Lim J W, Lin R S, Yang M H. Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008, 77(1): 125–141CrossRefGoogle Scholar
  39. 39.
    Dinh T B, Vo N, Medioni G. Context tracker: exploring supporters and distracters in unconstrained environments. In: proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2011, 1177–1184Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Nan Ren
    • 1
  • Junping Du
    • 1
  • Suguo Zhu
    • 1
  • Linghui Li
    • 1
  • Dan Fan
    • 1
  • JangMyung Lee
    • 2
  1. 1.Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Department of Electronics EngineeringPusan National UniversityBusanKorea

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