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Visual object tracking with online sample selection via lasso regularization

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Abstract

In the past years, discriminative methods are popular in visual tracking. The main idea of the discriminative method is to learn a classifier to distinguish the target from the background. The key step is the update of the classifier. Usually, the tracked results are chosen as the positive samples to update the classifier, which results in the failure of the updating of the classifier when the tracked results are not accurate. After that the tracker will drift away from the target. Additionally, a large number of training samples would hinder the online updating of the classifier without an appropriate sample selection strategy. To address the drift problem, we propose a score function to predict the optimal candidate directly instead of learning a classifier. Furthermore, to solve the problem of a large number of training samples, we design a sparsity-constrained sample selection strategy to choose some representative support samples from the large number of training samples on the updating stage. To evaluate the effectiveness and robustness of the proposed method, we implement experiments on the object tracking benchmark and 12 challenging sequences. The experiment results demonstrate that our approach achieves promising performance.

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References

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

  2. Chen, Z., You, X., Zhong, B., Li, J., Tao, D.: Dynamically Modulated Mask Sparse Tracking. IEEE Trans. Cybern. doi:10.1109/TCYB.2016.2577718

  3. Danelljan, M., Häger ,G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, pp. 1–11 (2014)

  4. Gao, J., Ling, H., Hu, W., Xing, J.: Transfer learning based visual tracking with gaussian processes regression. In: Computer Vision—ECCV, pp. 188–203 (2014)

  5. Han, Z., Jiao, J., Zhang, B., Ye, Q., Liu, J.: Visual object tracking via sample-based adaptive sparse representation (adasr). Pattern Recognit. 44(9), 2170–2183 (2011)

    Article  Google Scholar 

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

  7. He, Z., Yi, S., Cheung, Y.-M., You, X., Tang, Y.Y.: Robust object tracking via key patch sparse representation. IEEE Trans. Cybern. 1–11 (2016). doi:10.1109/TCYB.2016.2514714

  8. He, Z., Li, X., You, X., Tao, D., Tang, Y.Y.: Connected Component Model for Multi-Object Tracking. IEEE Transactions on Image Processing. 25(8), 3698–3711 (2016)

    Article  MathSciNet  Google Scholar 

  9. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715 (2012)

  10. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  11. 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. IEEE (2012)

  12. Jing, X.-Y., Wu, F., Zhu, X., Dong, X., Ma, F., Li, Z.: Multi-spectral low-rank structured dictionary learning for face recognition. Pattern Recogn. 59, 14–25 (2016)

  13. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)

  14. Koh, K., Kim, S., Boyd, S.: l1 ls: a matlab solver for large-scale l1-regularized least squares problems. Stanford University, pp. 1–6 (2007)

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

  16. Li, X., Liu, Q., He, Z., Wang, H., Zhang, C., Chen, W.-S.: A multi-view model for visual tracking via correlation filters. Knowl.-Based Syst. 113, 88–99 (2016)

    Article  Google Scholar 

  17. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1313–1320. IEEE (2011)

  18. Ma, X., Liu, Q., He, Z., Zhang, X., Chen, W.-S.: Visual tracking via exemplar regression model. Knowl.-Based Syst. 106, 26–37 (2016)

    Article  Google Scholar 

  19. Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: IEEE 12th International Conference on Computer Vision, pp. 1436–1443 (2009)

  20. Ou, W., You, X., Tao, D., Zhang, P., Tang, Y., Zhu, Z.: Robust face recognition via occlusion dictionary learning. Pattern Recogn. 47(4), 1559–1572 (2014)

    Article  Google Scholar 

  21. Ou, W., Yu, S., Li, G., Lu, J., Zhang, K.: Xie, G: Multi-view non-negative matrix factorization by patch alignment framework with view consistency. Neurocomputing 204, 116–124 (2016)

    Article  Google Scholar 

  22. Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications, vol. 685. Artech House, Boston (2004)

    MATH  Google Scholar 

  23. Ross, D.A., 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 

  24. Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 723–730 (2010)

  25. Shan, D., Zhang, C.: Visual tracking using ipca and sparse representation. SIViP 9(4), 913–921 (2015)

    Article  Google Scholar 

  26. Wang, X., Wang, Y., Wan, W., Hwang, J.-N.: Object tracking with sparse representation and annealed particle filter. SIViP 8(6), 1059–1068 (2014)

    Article  Google Scholar 

  27. Wang, N., Shi, J., Yeung, D.-Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3101–3109 (2015)

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

  29. Wu, F., Jing, X.-Y., You, X., Yue, D., Hu, R., Yang, J.-Y.: Multi-view low-rank dictionary learning for image classification. Pattern Recogn. 50, 143–154 (2016)

    Article  Google Scholar 

  30. Yi, S., Lai, Z., He, Z., Cheung, Y.-M., Liu, Y.: Joint sparse principal component analysis. Pattern Recogn. 61, 524–536 (2017)

  31. Zhu, G., Wang, J., Wu, Y., Lu, H.: Collaborative correlation tracking. In: Proceedings of British Machine Vision Conference, pp. 184.1–184.12 (2015)

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (No. 61402122), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qianjiao KY word [2015]487).

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Correspondence to Weihua Ou.

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Liu, Q., Ma, X., Ou, W. et al. Visual object tracking with online sample selection via lasso regularization. SIViP 11, 881–888 (2017). https://doi.org/10.1007/s11760-016-1035-x

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  • DOI: https://doi.org/10.1007/s11760-016-1035-x

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