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Adaptive Model Updating Correlation Filter Tracker with Feature Fusion

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Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12306))

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Abstract

Aiming at the poor accuracy of a single feature in the challenging scenarios, as well as the failure of tracking caused by partial or complete occlusion and background clutter, a correlation filter tracking algorithm based on feature fusion and model adaptive updating is proposed. On the basis of the background-aware correlation filter, the proposed algorithm firstly introduces the CN feature and integrates with the HOG feature to improve the accuracy of tracking. Then, the Average Peak-to-Correlation Energy (APCE) is introduced, and the results of object tracking are fed back to the tracker through the ratio changes. The tracker is adaptively updated, which improves the robustness of the algorithm to occlusion and background clutter. Finally, the proposed algorithm is experimented on the self-build ship dataset. The experimental results show that the algorithm can adapt well to complex scenes, such as object occlusion and background clutter. Compared to the state-of-the-art trackers, the average precision of the proposed tracker is improved by 2.3%, the average success rate is improved by 2.9%, and the average speed is about 18 frames per second.

J. Shao—Student.

This study was financially supported by the Natural Science Foundation of Zhejiang Province Major Project (LZ20F020004), the Science and Technology Plan Major Science and Technology Projects of Wenzhou (ZY2019020), the Natural Science Foundation of Zhejiang Province (LY16F020022), and Wenzhou Science and Technology Planning Project (S20180017) of China.

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References

  1. Xiao, L., Xu, M., Hu, Z.: Real-time inland CCTV ship tracking. Math. Probl. Eng. 2018(2018), 1–10 (2018)

    Google Scholar 

  2. Xiao, L., Wang, H., Hu, Z.: Visual tracking via adaptive random projection based on sub-regions. IEEE Access 6, 41955–41965 (2018)

    Article  Google Scholar 

  3. Hu, Z., Xiao, L., Teng, F.: An overview of compressive trackers. Int. J. Signal Process. Image Process. Pattern Recogn. 9(9), 113–122 (2016)

    Google Scholar 

  4. Hu, Z., Zou, M., Chen, C., Wu, Q.: Tracking via context-aware regression correlation filter with a spatial-temporal regularization. J. Electron. Imaging 29(2), 023029 (2020)

    Article  Google Scholar 

  5. Yun, S., Choi, J., Yoo, Y., Yun, K., Young Choi, J.: Action-decision networks for visual tracking with deep reinforcement learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2711–2720 (2017)

    Google Scholar 

  6. Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2805–2813 (2017)

    Google Scholar 

  7. Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074–3082 (2015)

    Google Scholar 

  8. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional Siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56

    Chapter  Google Scholar 

  9. Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1396–1404 (2017)

    Google Scholar 

  10. Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.H.: Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4904–4913 (2018)

    Google Scholar 

  11. Lukezic, A., Vojir, T., Cehovin Zajc, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6309–6318 (2017)

    Google Scholar 

  12. Tang, M., Yu, B., Zhang, F., Wang, J.: High-speed tracking with multi-kernel correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4874–4883 (2018)

    Google Scholar 

  13. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_50

    Chapter  Google Scholar 

  14. Bolme, D.S., Beveridge, J.R., Draper, B.A., 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)

    Google Scholar 

  15. 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 (2014)

    Article  Google Scholar 

  16. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_18

    Chapter  Google Scholar 

  17. Danelljan, M., Shahbaz Khan, F., Felsberg, M., Van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090–1097 (2014)

    Google Scholar 

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

    Google Scholar 

  19. Kristan, M., et al.: The visual object tracking VOT2014 challenge results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 191–217. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_14

    Chapter  Google Scholar 

  20. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4310–4318 (2015)

    Google Scholar 

  21. Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.: Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401–1409 (2016)

    Google Scholar 

  22. Kiani Galoogahi, H., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1135–1143 (2017)

    Google Scholar 

  23. Wang, Q., Gao, J., Xing, J., Zhang, M., Hu, W.: DCFNet: discriminant correlation filters network for visual tracking. arXiv preprint arXiv:1704.04057 (2017)

  24. Wu, Q., Yan, Y., Liang, Y., Liu, Y., Wang, H.: DSNet: deep and shallow feature learning for efficient visual tracking. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 119–134. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20873-8_8

    Chapter  Google Scholar 

  25. Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4021–4029 (2017)

    Google Scholar 

  26. Kiani Galoogahi, H., Sim, T., Lucey, S.: Multi-channel correlation filters. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3072–3079 (2013)

    Google Scholar 

  27. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China

    Jingjing Shao & Lei Xiao

  2. Intelligent Information Systems Institute, Wenzhou University, Wenzhou, China

    Zhongyi Hu

Authors
  1. Jingjing Shao
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  2. Lei Xiao
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  3. Zhongyi Hu
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Corresponding author

Correspondence to Zhongyi Hu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Shao, J., Xiao, L., Hu, Z. (2020). Adaptive Model Updating Correlation Filter Tracker with Feature Fusion. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_22

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  • DOI: https://doi.org/10.1007/978-3-030-60639-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60638-1

  • Online ISBN: 978-3-030-60639-8

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