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Joint Gabor histogram and vector graph association for aerial target anti-interference tracking

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Abstract

Aerial targets subjected to artificial infrared interference exhibit phenomena such as occlusion, adhesion, and similarity. The challenges in actual engineering applications include significant changes in the shape, scale, and radiation characteristics of a target due to its maneuvering and relative motion. To solve these issues, we propose an infrared object anti-interference tracking algorithm based on Gabor histogram and vector graph association. First, Gabor features are constructed, and a low-dimensional Gabor-direction histogram is established to describe the texture distribution properties of local areas. Second, the characteristics and variation laws of the frequency-domain energy distribution are utilized to improve the accuracy of the target-scale information estimation. For the partial occlusion of the target, a tracking strategy based on a high-confidence block is applied to accurately estimate the overall position of the target. In cases where the target is completely occluded, a tracking strategy based on position vector graph association is proposed. A target-bait relative position vector graph association model is established through historical position and distribution information, so as to match and predict the target location. Under the condition of interference due to an infrared decoy, the experimental results show that the accuracy and running speed of the anti-interference tracking algorithm proposed in this paper reach 90.0% and 248.2 fps, respectively, which meet the requirements of engineering applications.

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References

  1. J. Deutscher, I. Reid, Articulated body motion capture by stochastic search. Int. J. Comput. Vis. 61(2), 185–205 (2005)

    Article  Google Scholar 

  2. Z.H. Huang, Y.Z. Zhang, X.F. Yue, X. Li, H. Fang, H.Y. Hong, T.X. Zhang, Joint horizontal-vertical enhancement and tracking scheme for robust contact-point detection from pantograph-catenary infrared images. Infrared Phys. Technol. 105, 103156 (2020)

    Article  Google Scholar 

  3. C. Shen, A. van den Hengel, A. Dick, M.J. Brooks, Enhanced importance sampling: unscented auxiliary particle filtering for visual tracking, in Proceedings of the 17th Australian Joint Conference on Advances in Artificial Intelligence, AI’04 (2004). pp. 180–191

  4. Z.H. Huang, L. Chen, Y.Z. Zhang, Z.X. Yu, H. Fang, T.X. Zhang, Robust contact-point detection from pantograph-catenary infrared images by employing horizontal-vertical enhancement operator. Infrared Phys. Technol. 101, 146–155 (2019)

    Article  ADS  Google Scholar 

  5. T. Zhang, C. Xu, M.H. Yang, Multi-task correlation particle filter for robust object tracking, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). pp. 4819–4827

  6. J. Carpenter, P. Clifford, P. Fearnhead, Improved particle filter for nonlinear problems. Radar Sonar Navig. IEE Proceed. 146, 2–7 (1999)

    Article  Google Scholar 

  7. D.S. Bolme, J.R. Beveridge, B.A. Draper, Y.M. Lui, Visual object tracking using adaptive correlation filters, in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2020). pp. 2544–2550

  8. J.F. Henriques, R. Caseiro, P. Martins, J. Batista, High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  9. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, (2005). 886–893

  10. Y. Li, J. Zhu, A scale adaptive kernel correlation filter tracker with feature integration. in Computer Vision - ECCV 2014 Workshops, ed by L. Agapito, M.M. Bronstein, C. Rother (2015). pp. 254–265

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

  12. Z.H. Huang, Y.Z. Zhang, Q. Li, Z.T. Li, T.X. Zhang, N. Sang, S.Q. ** and denoising optical remote sensing images. Int. J. Remote Sens. 40(15), 5737–5748 (2019)

    Article  Google Scholar 

  13. M. Danelljan, A. Robinson, F. Shahbaz Khan, M. Felsberg, Beyond correlation filters: learning continuous convolution operators for visual tracking. in Computer Vision – ECCV 2016, ed by B. Leibe, J. Matas, N. Sebe, M. Wellin (2016). pp. 472–488

  14. M. Danelljan, G. Bhat, F.S. Khan, M. Felsberg, Eco: efficient convolution operators for tracking, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). pp. 6931–6939

  15. B. Li, J. Yan, W. Wu, Z. Zhu, X. Hu, High performance visual tracking with siamese region proposal network, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

  16. Q. Wang, L. Zhang, L. Bertinetto, W. Hu, P.H. Torr, Fast online object tracking and segmentation: a unifying approach. in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019). pp. 1328–1338

  17. L. Zhang, A. Gonzalez-Garcia, J.V.D. Weijer, M. Danelljan, F.S. Khan, Learning the model update for siamese trackers, in The IEEE International Conference on Computer Vision (ICCV) (2019)

  18. C. Ma, X. Yang, C. Zhang, M.H. Yang, Long-term correlation tracking, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015). pp. 5388–5396

  19. M. Danelljan, G. Hager, F.S. Khan, M. Felsberg, Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. O. Akin, E. Erdem, A. Erdem, K. Mikolajczyk, Deformable part-based tracking by coupled global and local correlation filters. J. Vis. Commun. Image Represent. 38, 763–774 (2016)

    Article  Google Scholar 

  22. B. Babenko, M.H. Yang, S. Belongie, Visual tracking with online multiple instance learning, in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 983–990

  23. S. Hare, S. Golodetz, A. Saffari, V. Vineet, M.M. Cheng, S.L. Hicks, P.H.S. Torr, Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)

    Article  Google Scholar 

  24. Y. Lu, T. Wu, S.C. Zhu, Online object tracking, learning, and parsing with and-or graphs. in 2014 IEEE Con-ference on Computer Vision and Pattern Recognition (2014). pp. 3462–3469

  25. M. Danelljan, G. Häger, F.S. Khan, M. Felsberg, Learning spatially regularized correlation filters for visual tracking. in 2015 IEEE International Conference on Computer Vision (ICCV) (2015). pp. 4310–4318

  26. K. Dai, D. Wang, H. Lu, C. Sun, J. Li, Visual tracking via adaptive spatially-regularized correlation filters. in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019). pp. 4665–4674

  27. J.G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 1160–1169 (1985)

    Article  ADS  Google Scholar 

  28. J. Daugman, C. Downing, Gabor Wavelets for Statistical Pattern Recognition (MIT Press, Cambridge, MA, USA, 1998), pp. 414–420

    Google Scholar 

  29. V. Kyrki, J.K. Kamarainen, H. Kälviäinen, Simple gabor feature space for invariant object recognition. Pattern Recognit. Lett. 25(3), 311–318 (2004)

    Article  ADS  Google Scholar 

  30. N. Wang, W. Zhou, Q. Tian, R. Hong, M. Wang, H. Li, Multi-cue correlation filters for robust visual tracking, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018), pp. 4844–4853

  31. Y. Li, J. Zhu, S.C. Hoi, Reliable patch trackers: robust visual tracking by exploiting reliable patches, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015). pp. 353–361

  32. M. Mueller, N. Smith, B. Ghanem, Context-aware correlation filter tracking, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), pp. 1387–1395

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61703337) and by the Aviation Science Foundation of China (Grant No. ASFC-20191053002).

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Authors and Affiliations

  1. School of Astronautics, Northwestern Polytechnical University, **’an, China

    Shaoyi Li, ** Yang & Guodong Fu

  2. Shanghai Academy of Spaceflight Technology, Shanghai, China

    Junyan Yang

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  1. Shaoyi Li
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  2. Junyan Yang
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  3. ** Yang
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  4. Guodong Fu
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Correspondence to Shaoyi Li.

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Li, S., Yang, J., Yang, X. et al. Joint Gabor histogram and vector graph association for aerial target anti-interference tracking. J Opt 51, 227–240 (2022). https://doi.org/10.1007/s12596-021-00816-6

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