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Image object tracking based on temporal context and MOSSE

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Abstract

Designing an image target tracking algorithm which is suitable for all occasions is a hotspot in visual field. The MOSSE algorithm based on correlation filter achieves good tracking effect, but it has the disadvantage of poor anti-drift ability. Based on the MOSSE algorithm, multi-frame historical images are used as the input samples of AdaBoost, the classification effect of the weak classifier is measured by the response to the peak coordinate distance, the weights of the training samples are updated according to the classification effect, and multiple weak classifiers and then weighed the weak classifiers according to the accuracy of the tracking target, then we get the final strong filter. The algorithm makes full use of the historical appearance information of the target, which can improve the robustness of the system effectively, while still maintaining the real-time of the related filter algorithm.

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References

  1. Ross, D.A., Lim, J., Lin, R.S., et al.: Incremental Learning for Robust Visual Tracking. Int. J. Comput. Vis. 77, 125–141 (2008). doi:10.1007/s11263-007-0075-7

    Article  Google Scholar 

  2. Wang, D., Lu, H., Yang, M.H.: Least soft-threshold squares tracking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013, pp. 2371–2378. doi:10.1109/CVPR.2013.307

  3. Mei, X., Hong, Z., Prokhorov, D., Tao, D.: Robust multitask multiview tracking in videos. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2874–2890 (2015). doi:10.1109/TNNLS.2015.2399233

    Article  MathSciNet  Google Scholar 

  4. Mei, X., Ling, H., Wu, Y., Blasch, E.P., Bai, L.: Efficient minimum error bounded particle resampling L1 tracker with occlusion detection. IEEE Trans. Image Process. 22(7), 2661–2675 (2013). doi:10.1109/TIP.2013.2255301

    Article  MathSciNet  MATH  Google Scholar 

  5. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011). doi:10.1109/TPAMI.2011.66

    Article  MathSciNet  Google Scholar 

  6. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003). doi:10.1109/TPAMI.2003.1195991

  7. Avidan, S.: Ensemble tracking. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (2005), vol. 2, pp. 494–501. doi:10.1109/CVPR.2005.144

  8. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010), pp. 49–56. doi:10.1109/CVPR.2010.5540231

  9. Bai, Q., Wu, Z., Sclaroff, S, Betke, M., Monnier, C.: Randomized ensemble tracking. In: 2013 IEEE International Conference on Computer Vision (2013), pp. 2040–2047

  10. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking, Volume 5302 of the series Lecture Notes in Computer Science pp 234–247 (2008). doi:10.1007/978-3-540-88682-2_19

  11. Grabner, H., Bischof, H.: On-line boosting and vision. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), 2006, pp. 260–267. doi:10.1109/CVPR.2006.215

  12. Zhou, Q., Luo, J.: The study on evaluation method of urban network security in the big data era. Intell. Autom. Soft Comput. (2017). doi:10.1080/10798587.2016.1267444

  13. Zeisl, B., Leistner, C., Saffari, A., Bischof, H.: On-line semi-supervised multiple-instance boosting. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 1879–1879. doi:10.1109/CVPR.2010.5539860

  14. Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064–1072 (2004). doi:10.1109/TPAMI.2004.53

    Article  Google Scholar 

  15. 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). doi:10.1109/CVPR.2013.312

  16. Draper, B.A., Bolme, D.S., Beveridge, J.R., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010)

  17. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. Berlin, Volume 7575 of the series Lecture Notes in Computer Science, pp. 702–715 (2012). doi:10.1007/978-3-642-33765-9_50

  18. Danelljan, M., Shahbaz Khan F., Felsberg, M., et al.: Adaptive color attributes for real-time visual tracking. In: 2014 IEEE Conference on Computer Vision and Patter (2014), pp. 1090–1097

  19. 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). doi:10.1109/TPAMI.2014.2345390

    Article  Google Scholar 

  20. Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2105–2112 (2009)

  21. Mahalanobis, A., Kumar, B.V.K., Song, S., Sims, S.R.F., Epperson, J.F.: Unconstrained correlation filters. Appl. Opt. 33(17), 3751–3759 (1994). doi:10.1364/AO.33.003751

    Article  Google Scholar 

  22. Freund, Yoav, Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). doi:10.1006/jcss.1997.1504

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M-H.: Fast visual tracking via dense spatio-temporal context learning, Volume 8693 of the series Lecture Notes in Computer Science, pp. 127–141. doi:10.1007/978-3-319-10602-1_9

  24. Leichter, Ido, Lindenbaum, Michael, Rivlin, Ehud: Mean shift tracking with multiple reference color histograms. Comput. Vis. Image Underst. 114(3), 400–408 (2010). doi:10.1016/j.cviu.2009.12.006

    Article  Google Scholar 

  25. Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. IET Comput. Vis. 6(1), 52–61 (2012). doi:10.1049/iet-cvi.2010.0112

    Article  MathSciNet  Google Scholar 

  26. Alemu, A.Y., Pei, Z.J., Zhang, J.: Mean shift tracking with advanced background-weighted histogram. Appl. Mech. Mater. 302, 706–710 (2013). doi:10.4028/www.scientific.net/AMM.302.706

    Article  Google Scholar 

  27. Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. In: 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro (2007), pp. 1–8. doi:10.1109/ICCV.2007.4408954

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Authors and Affiliations

  1. School of Software, North China University of Water Resources and Electric Power, Zhengzhou, 450045, Henan, China

    Ke Han

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  1. Ke Han
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Correspondence to Ke Han.

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Han, K. Image object tracking based on temporal context and MOSSE. Cluster Comput 20, 1259–1269 (2017). https://doi.org/10.1007/s10586-017-0800-0

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  • DOI: https://doi.org/10.1007/s10586-017-0800-0

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