Skip to main content
Springer Nature Link
Log in

Object tracking based on support vector dictionary learning

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Dictionary learning is widely used to track targets in video sequences. However, a target can be lost during the tracking because of rotation, motion, background clutter, and so on. A dictionary learning method has recently been developed to reduce the chances of missing the target. We developed a new approach using support vector dictionary learning with histograms of sparse codes for a particle filter framework. The representation with support vector can help balance the residual between the candidate and the target. The experiments conducted on challenging sequences demonstrate that the proposed method outperforms seven state-of-the-art algorithms in terms of the overlap rate, center error, and accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Salti, S., Cavallaro, A., Stefano, L.D.: Adaptive appearance modeling for video tracking: survey and evaluation. IEEE Trans. Image Process. 21(10), 4334–4348 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. Salti, S., Cavallaro, A., Stefano, L.D.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)

    Article  Google Scholar 

  3. Zhang, B., Luan, S., Chen, C., Han, J., Wang, W., Perina, A., Shao, L.: Latent constrained correlation filter. IEEE Trans. Image Process. 27(3), 1038–1048 (2018)

    Article  MathSciNet  Google Scholar 

  4. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (06 CVPR), pp. 798–805 (2006)

  5. Nejhum, S.M.S., Ho, J., Yang, M.-H.: Online visual tracking with histograms and articulating block. Comput. Vis. Image Underst. 114(8), 901–914 (2010)

    Article  Google Scholar 

  6. Hu, W., Li, X., Luo, W., Zhang, X., Maybank, S., Zhang, Z.: Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model. IEEE Trans. Pattern Anal. Mach. Intell. 34(23), 2420–2440 (2012)

    Google Scholar 

  7. Zhang, B., Li, Z., Cao, X., Ye, Q., Chen, C., Shen, L., Perina, A., Ji, R.: Output constraint transfer fo kernelized correlation filter in tracking. IEEE Trans. Syst. Man Cybern. Syst. 47(4), 693–703 (2017)

    Article  Google Scholar 

  8. Cai, S., Zuo, W., Zhang, L., Feng, X., Wang, P.: Support vector guided dictionary learning. In: European Conference on Computer Vision, pp. 624–639. Springer (2014)

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

    Article  Google Scholar 

  10. Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (13 CVPR), pp. 2411–2418 (2013)

  11. Zhao, Q., Yang, Z., Tao, H.: Differential earth mover’s distance with its applications to visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 274–87 (2010)

    Article  Google Scholar 

  12. Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. In: IEEE Computer Society on IEEE Conference on Computer Vision and Pattern Recognition (07 CVPR), pp. 1–8 (2007)

  13. Jiang, S., Ning, J., Cai, C., Li, Y.: Robust Struck tracker via color Haar-like feature and selective updating. SIViP 11(6), 1073–1080 (2017)

    Article  Google Scholar 

  14. Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust object tracking using joint color-texture histogram. Int. J. Pattern Recognit. Artif. Intell. 23(7), 1245–1263 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Lim, H., Morariu, V.I., Camps, O.I., Sznaier, M.: Dynamic appearance modeling for human tracking. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (12 CVPR), pp. 751–757 (2006)

  17. Leichter, I., Lindenbau, M., Rivlin, E.: Tracking by affine kernel transformations using color and boundary cues. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 164–171 (2009)

    Article  Google Scholar 

  18. Wang, H., Suter, D., Schindler, K., Shen, C.: Adaptive object tracking based on an effective appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1661–1667 (2007)

    Article  Google Scholar 

  19. Yang, J., Yang, M.-H.: Top-down visual saliency via joint CRF and dictionary learning. In: IEEE Computer Society on IEEE Conference on Computer Vision and Pattern Recognition (12 CVPR), pp. 2296–2303 (2012)

  20. Wang, S., Lu, H., Yang, F., Yang, M.-H.: Superpixel tracking. In: IEEE Computer Society on International Conference on Computer Vision (2011 ICCV), pp. 1323–1330 (2011)

  21. Wang, C., Xu, W., Pei, X.F., Zhou, X.Y.: An unsupervised multi-scale segmentation method based on automated parameterization. Arab. J. Geosci. 9(15), 651 (2016)

    Article  Google Scholar 

  22. Yi, O.: Structural sparse coding seeds-active appearance model for object tracking. SIViP 11(6), 1097–1104 (2017)

    Article  Google Scholar 

  23. Zhang, L., Wu, W., Chen, T., Strobel, N., Coaniciu, D.: Robust object tracking using semi-supervised appearance dictionary learning. Pattern Recognit. Lett. 25(1), 17–23 (2015)

    Article  Google Scholar 

  24. Yang, Y., Li, M., Nian, F., Zhao, H., He, Y.: Vision target tracker based on incremental dictionary learning and global and local classification. Abstr. Appl. Anal. (2013). https://doi.org/10.1155/2013/323072

    MathSciNet  MATH  Google Scholar 

  25. Ren, X., Ramanan, D.: Histograms of sparse codes for object detection. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (13 CVPR), pp. 3246–3253 (2013)

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

    Article  Google Scholar 

  27. Lv, L., Fan, T.H., Sun, Z., Wang, J., Xu, L.Z.: Object tracking with double-dictionary appearance model. Opt. Eng. 55, 083106 (2016)

    Article  Google Scholar 

  28. Yang, F., Lu, H., Yang, M.-H.: Robust superpixel tracing. IEEE Trans. Signal Process. 23(4), 1639–1651 (2014)

    MATH  Google Scholar 

  29. Babenko, B., Yang, M.-H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)

    Article  Google Scholar 

  30. Wang, D., Lu, H.C., Yang, M.-H.: Online object tracking with sparse prototypes. IEEE Trans. Image Process. 22(1), 314–325 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  31. Xu, J., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (12 CVPR), pp. 1822–1829 (2012)

  32. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: European Conference on Computer Vision (12 ECCV), pp. 864–877 (2012)

  33. He, S., Yang, Q., Lau, R., Wang, J., Yang, M.-H.: Visual tracking via locality sensitive histograms. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (13 CVPR), pp. 2427–2434 (2013)

  34. Oron, S., Bar-Hillel, A., Levi, D., Avidan, S.: Locally orderless tracking. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (12 CVPR), pp. 1940–1947 (2012)

  35. Wang, D., Lu, H., Yang, M.-H.: Least soft-threshold squares tracking. In: IEEE Computer Society on Conference on Computer Vision and Pattern Recognition (13 CVPR), pp. 2371–2378 (2013)

  36. Everingham, M., Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61563036, 61501173, and 61461032 and Natural Science Foundation of Jiangxi Province under Grant No. 20161BAB212037.

Author information

Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, 210098, China

    Li Lv, Zhe Chen, Zhen Zhang & Lizhong Xu

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Li Lv & Tanghuai Fan

Authors
  1. Li Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tanghuai Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lizhong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lizhong Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, L., Chen, Z., Zhang, Z. et al. Object tracking based on support vector dictionary learning. SIViP 12, 1189–1196 (2018). https://doi.org/10.1007/s11760-018-1270-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1270-4

Keywords