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Structural sparse coding seeds–active appearance model for object tracking

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Abstract

In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance model. The proposed object tracking scheme involves three stages: training, detection and tracking. In the training stage, an incremental SVM model that directly measures the candidates samples and target difference. The proposed mixture generate–discriminative method can well separate two highly correlated positive candidates images. In the detection stage, the trained weighted vector is used to separate the target object in positive candidates images with respect to the seeds images. In the tracking stage, we employ the particle filter to track the object through an appearance adaptive updating algorithm with seeds–active constrained sparse representation. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the current literature.

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References

  1. Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1436–1443 (2009)

  2. Zhang, S., Yao, H., Zhou, H., Sun, X., Liu, S.: Robust visual tracking based on online learning sparse representation. Neurocomputing 100(1), 31–40 (2013)

    Article  Google Scholar 

  3. Zhang, S., Yao, H., Sun, X., Lu, X.: Sparse coding based visual tracking: review and experimental comparison. Pattern Recognit. 46(7), 1772–1788 (2013)

    Article  Google Scholar 

  4. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: Computer Vision and Pattern Recognition 2012 IEEE Conference on, pp. 1822–1829 (2012)

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

    Article  Google Scholar 

  6. Zhu, G., Wang, J., Wu, Y., Lu, H.: Collaborative correlation tracking. In: Proceedings of the British Machine Vision Conference (BMVC), (184), pp. 1–12 (2015)

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

  8. Lan, X., Zhang, S., Yuen, P.C.: Robust joint discriminative feature learning for visual tracking. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. pp. 3403–3410 (2016)

  9. Zhang, S., Zhou, H., Jiang, F., Li, X.: Robust visual tracking using structurally random projection and weighted least squares. IEEE Trans. Circuits Syst. Video Technol. 25(11), 1749–1760 (2015)

    Article  Google Scholar 

  10. Zhang, S., Lan, X., Qi, Y., Yuen, P.C.: Robust visual tracking via basis matching. IEEE Trans. Circuits Syst. Video Technol. 99, 1–1 (2016)

    Google Scholar 

  11. Qi, Y., Zhang, S., Qin, L., Huang, Q., Yao, H., Lim, J., Yang, M.-H.: Hedged deep tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4303–4311 (2016)

  12. Kalal, Z., Matas, J., Mikolajczyk, K.: PN learning: bootstrapping binary classifiers by structural constraints. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 49–56 (2010)

  13. Lan, X., Ma, A.J., Yuen, P.C.: Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1194–1201 (2014)

  14. Lan, X., Ma, A.J., Yuen, P.C., Chellappa, R.: Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans. Image Process. 24(12), 5826–5841 (2015)

    Article  MathSciNet  Google Scholar 

  15. Liu, R., Lan, X., Yuen, P.C., Feng, G.-C.: Robust visual tracking using dynamic feature weighting based on multiple dictionary learning. In: 2016 24th European Signal Processing Conference, pp. 2166–2701 (2016)

  16. Demir, H.S., Cetin, A.E.: Co-difference based object tracking algorithm for infrared videos. In: IEEE International Conference on Image Processing, pp. 434–438 (2016)

  17. Wang, S., Lu, H., Yang, F., et al.: Superpixel tracking. In: 2011 IEEE international conference on Computer Vision, pp. 1323–1330 (2011)

  18. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D., Li, X.: A biologically inspired appearance model for robust visual tracking. In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14 (2016)

  19. Ross, D.A., Lim, J., Lin, R.S., et al.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  20. Seo, J.-W., Kim, S.D.: Dynamic background subtraction via sparse representation of dynamic textures in a low-dimensional subspace. Signal Image Video Process. 10(1), 29–36 (2016)

    Article  Google Scholar 

  21. Diehl, C.P., Cauwenberghs, G.: SVM incremental learning, adaptation and optimization. In: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 4, pp. 2685–2690 (2003)

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

  23. Yang, J., Yu, K., Gong, Y., et al.: Linear spatial pyramid matching using sparse coding for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1794–1801 (2009)

  24. Wang, Y.-X., Xu, H., Ma, T.-H.: The auxiliary handoff mechanism based on context and particle filter algorithms. J. Electron. Inf. Technol. 34(1), 223–226 (2012)

    Google Scholar 

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

  26. Rodriguez, M.D., Ahmed, J., Shah, M.: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

  27. Oron, S., Bar-Hillel, A., Levi, D., et al.: Locally orderless tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1940–1947 (2012)

  28. Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust object tracking using joint color-texture histogram. Int. J. Pattern Recognit. Artif. Intell. 23(07), 1245–1263 (2009)

    Article  Google Scholar 

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

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Acknowledgements

This work is supported by the Projects of Zhejiang Province Science and Technology Plan (No. 2015C33071) and Engineering Research Center of Intelligent Transport of Zhejiang Province (No. 2015RCITZJ-KF1).

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  1. School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China

    Yi Ouyang

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  1. Yi Ouyang
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Correspondence to Yi Ouyang.

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Ouyang, Y. Structural sparse coding seeds–active appearance model for object tracking. SIViP 11, 1097–1104 (2017). https://doi.org/10.1007/s11760-017-1063-1

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