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Robust tracking via discriminative sparse feature selection

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Abstract

In this paper, we propose a novel generative tracking approach based on discriminative sparse feature selection. The sparse features are the discriminative sparse representation of samples, which are achieved by learning a compact and discriminative dictionary. Besides the target templates, the proposed approach also incorporates the close-background templates to approximate the partial variations. We learn the dictionary and a classifier together, and search the tracking result with the maximum similarity and the minimal reconstruction error criterion using the discrimination of sparse features. In addition, we resample the close-background templates and update the dictionary in an adaptive way during tracking. Experimental results on several challenging video sequences demonstrate that the proposed approach has more favorable performance than the state-of-the-art approaches.

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References

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

  2. Jiang, Z., Lin, Z., Davis, L.S.: Learning a discriminative dictionary for sparse coding via label consistent K-SVD. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1697–1704 (2011)

  3. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust L1 tracker using accelerated proximal gradient approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837 (2012)

  4. Babenko, B., Yang, M., Belongie, S.: Visual tracking with online multiple instance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 983–990 (2009)

  5. Wu, H., Li, G., Luo, X.: Weighted attentional blocks for probabilistic object tracking. Vis. Comput. 30(2), 229–243 (2014)

  6. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2011)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  8. 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 (CVPR), pp. 1822–1829 (2012)

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

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

  11. Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient L1 tracker with occlusion detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1257–1264 (2011)

  12. Quan, W., Chen, J.X., Yu, N.Y.: Robust object tracking using enhanced random ferns. Vis. Comput. 30(4), 351–358 (2014)

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

  14. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49–56 (2010)

  15. Comaniciu, D., Member, V.R., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)

    Article  Google Scholar 

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

  17. Grabner, H., Grabner, M., Bischof, H.: Real-Time Tracking via On-line Boosting. Proceedings of British Machine Vision Conference (BMVC) 1(5), 47–56 (2006)

    Google Scholar 

  18. Black, M., Jepson, A.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 26(1), 63–84 (1998)

    Article  Google Scholar 

  19. Matthews, L., Ishikawa, T., Baker, S.: The template update problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 810–815 (2004)

    Article  Google Scholar 

  20. Pang, Y., Ling, H.: Finding the best from the second bests—inhibiting subjective bias in evaluation of visual tracking algorithms. IEEE International Conference on Computer Vision (ICCV), pp. 2784–2791 (2013)

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

  22. Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Process. 13(11), 1491–1506 (2004)

    Article  Google Scholar 

  23. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  24. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 234–247 (2008)

  25. Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064–1072 (2004)

    Article  Google Scholar 

  26. Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)

  27. Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 661–675 (2002)

  28. Hu, W., Li, X., Zhang, X., Shi, X., Maybank, S.J., Zhang, Z.: Incremental tensor subspace learning and its applications to foreground segmentation and tracking. Int. J. Comput. Vis. 91(3), 303–327 (2011)

    Article  MATH  Google Scholar 

  29. Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on Lie algebra. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 728–735 (2006)

  30. Xue, M., Zhou, S.K., Porikli, F.: Probabilistic visual tracking via robust template matching and incremental subspace update. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME), pp. 1818–1821 (2007)

  31. Matthews, I., Baker, S.: Active appearance models revisited. Int. J. Comput. Vis. 60(2), 135–164 (2004)

    Article  Google Scholar 

  32. Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1728–1740 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Discriminative learned dictionaries for local image analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)

  35. Zhang, Q., Li, B.: Discriminative k-svd for dictionary learning in face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2691–2698 (2010)

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Acknowledgments

This research is supported by NSFC-Guangdong Joint Fund (No. U1135005, U1201252), the National Natural Science Foundation of China (No. 61320106008), Science and Technology Planning Project of Guangdong Province (No. 2012B010900009, 2012B010900089).

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Authors and Affiliations

  1. National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Jin Zhan, Zhuo Su, Hefeng Wu & Xiaonan Luo

  2. Institute of Dongguan-Sun Yat-sen University, Dongguan,  523000, China

    Zhuo Su

  3. Institute of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Jin Zhan

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  1. Jin Zhan
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  2. Zhuo Su
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  3. Hefeng Wu
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  4. Xiaonan Luo
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Correspondence to Zhuo Su.

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Zhan, J., Su, Z., Wu, H. et al. Robust tracking via discriminative sparse feature selection. Vis Comput 31, 575–588 (2015). https://doi.org/10.1007/s00371-014-0984-8

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