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Anti-occlusion object tracking based on correlation filter

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Abstract

Despite remarkable progress, visual object tracking is still a challenging task as objects usually suffer from significant appearance changes, fast motion, and serious occlusion. In this paper, we propose an anti-occlusion correlation filter-based tracking method (AO-CF) for robust visual tracking. We first propose an occlusion criterion based on continuous response values. Based on the criterion, objects are divided into four categories to adaptively identify the occlusion of objects. Then we propose a new detection condition for detecting proposals. When the occlusion criterion is triggered, the re-detection mechanism is executed and the tracker is commanded to stop, and then the re-detector selects the most reliable proposal to reinitialize the tracker. Experimental results show that our method outperforms other state-of-the-art trackers in terms of both precision rate and success rate on the widely used object tracking benchmark dataset. In addition, AO-CF is able to achieve real-time tracking speed.

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References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Bai, B., Li, Y., Fan, J., Price, C., Shen, Q.: Object tracking based on incremental Bi-2DPCA learning with sparse structure. Appl. Opt. 54(10), 2897–2907 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Zhang, K., Zhang, L., Yang, M.H.: Fast compressive tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2014)

    Article  Google Scholar 

  6. Zitnick, C.L., Dollar, P.: Edge boxes: locating object proposals from edges. In: European Conference on Computer Vision, pp. 391–405 (2014)

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

  8. Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  9. 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 

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

  11. Danelljan, M., Khan, F.S., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking, pp. 1090–1097 (2014)

  12. Galoogahi, H.K., Sim, T., Lucey, S.: Multi-channel correlation filters. In: International Conference Computer Vision, pp. 3072–3079 (2013)

  13. Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, pp. 1–5 (2014)

  14. Li, Y., Zhu, J.: A scale adaptive Kernel correlation filter tracker with feature integration. In: European Conference on Computer Vision, pp. 254–265 (2014)

  15. Choi, J., Chang, H.J., Yun, S.: Attentional correlation filter network for adaptive visual tracking. In: International Conference on Computer Vision and Pattern Recognition, pp. 4828–4837 (2017)

  16. Wang, Q., Gao, J., Xing, J.: DCFNet: discriminant correlation filters network for visual tracking. arXiv preprint arXiv:1704.04057 (2017)

  17. Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M.M., Hicks, S.L., Torr, P.H.S.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2015)

    Article  Google Scholar 

  18. Chen, D., Yuan, Z., Wu, Y., Zhang, G., Zheng, N.: Constructing adaptive complex cells for robust visual tracking. In: IEEE International Conference on Computer Vision, pp. 1113–1120 (2013)

  19. Holzer, S., Llic, S., Navab, N.: Multilayer adaptive linear predictors for real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 105–117 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Zhang, J., Ma, S., Sclaroff, S.: Meem: robust tracking via multiple experts using entropy minimization. In: European Conference on Computer Vision, pp. 188–203 (2014)

  22. Zhang, B., Li, Z., Cao, X.: Output constraint transfer for kernelized correlation filter in tracking. IEEE Trans. Syst. Man Cybern. 47(4), 693–703 (2017)

    Article  Google Scholar 

  23. Zhang, B., Luan, S., Chen, C.: Latent constrained correlation filter. IEEE Trans. Image Process. 27(3), 1038–1048 (2018)

    Article  MathSciNet  Google Scholar 

  24. Zhao, J., Xiao, G., Zhang, X., Bavirisetti, D.: An improved long-term correlation tracking method with occlusion handling. Chin. Opt. Lett. (2019). https://doi.org/10.3788/COL201917.031001

    Article  Google Scholar 

  25. Ma, C., Yang, X., Zhang, C.: Long-term correlation tracking. In: International Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)

  26. Ning, J., Yang, J., Jiang, S.: Object tracking via dual linear structured SVM and explicit feature map. In: International Conference on Computer Vision and Pattern Recognition, pp. 4266–4274 (2016)

  27. Bertinetto, L., Valmadre, J., Golodetz, S.: Staple: complementary learners for real-time tracking. In: International Conference on Computer Vision and Pattern Recognition, pp. 1401–1409 (2016)

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

  29. Kalal, Z., Mikolajczyk, K., Matas, J.: Forward–backward error: automatic detection of tracking failures. In: IEEE International Conference on Pattern Recognition, pp. 2756–2759 (2010)

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Acknowledgements

This paper is sponsored by National Program on Key Basic Research Project (2014CB744903), National Natural Science Foundation of China (61973212, 61673270), Shanghai Science and Technology Committee Research Project (17DZ1204304).

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jun Liu, Gang Xiao, Xingchen Zhang, Ping Ye & Shengyun Peng

  2. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, China

    Jun Liu & Xingzhong Xiong

  3. College of Civil Engineering, Tongji University, Shanghai, 20092, China

    Shengyun Peng

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  1. Jun Liu
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  2. Gang Xiao
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  3. Xingchen Zhang
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  4. Ping Ye
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Correspondence to Xingchen Zhang.

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Liu, J., Xiao, G., Zhang, X. et al. Anti-occlusion object tracking based on correlation filter. SIViP 14, 753–761 (2020). https://doi.org/10.1007/s11760-019-01601-6

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