Skip to main content
SpringerLink
Log in

UAV object tracking by background cues and aberrances response suppression mechanism

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Real-time object tracking for unmanned aerial vehicles (UAVs) is an essential and challenging research topic for computer vision. However, the scenarios that UAVs deal with are complicated, and the UAV tracking targets are small. Therefore, general trackers often fail to take full advantage of their performance in UAV scenarios. In this paper, we propose a tracking method for UAV scenes, which utilizes background cues and aberrances response suppression mechanism to track in 4 degrees-of-freedom. Firstly, we consider the tracking task as a similarity measurement problem. In this study, we decompose this problem into two subproblems for optimization. Secondly, to alleviate the problem of small targets in UAV scenes, we utilize background cues fully. Also, to reduce interference by background information, we employ an aberrance response suppression mechanism. Then, to obtain accurate target state information, we introduce a logarithmic polar coordinate system. We perform phase correlation calculations in logarithmic polar coordinates to obtain the rotation and scale changes of the target. Finally, target states are obtained through response fusion, which includes displacement, scale, and rotation angle. Our approach is carried out in a large number of experiments on various UAV datasets, such as UAV123, DBT70, and UAVDT2019. Compared with the current advanced trackers, our method is superior on UAV small target tracking.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PH (2016) Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1401–1409

  2. Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: Proceedings of the European conference on computer vision, Springer, pp 850–865

  3. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, pp 2544–2550

  4. Bonatti R, Ho C, Wang W, Choudhury S, Scherer S (2019) Towards a robust aerial cinematography platform: localizing and tracking moving targets in unstructured environments. arXiv preprint arXiv:1904.02319

  5. Chen L, Shao Y, Mei Y, Chu H, Chang Z, Zhan H, Rao Y, Yang G (2019) Using KCF and face recognition for outdoor target tracking UAV. In: Proceedings of the tenth international conference on graphics and image processing, vol 11069, International Society for Optics and Photonics, p 110690J

  6. Cheng H, Lin L, Zheng Z, Guan Y, Liu Z (2017) An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1732–1738

  7. Choi J, Jin Chang H, Yun S, Fischer T, Demiris Y, Young Choi J (2017) Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4807–4816

  8. Dai K, Wang D, Lu H, Sun C, Li J (2019) Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4670–4679

  9. Danelljan M, Bhat G, Shahbaz Khan F, Felsberg M (2017) Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6638–6646

  10. Danelljan M, Häger G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: Proceedings of the British machine vision conference

  11. Danelljan M, Häger G, Khan FS, Felsberg M (2016) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575

    Article  Google Scholar 

  12. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 4310–4318

  13. Fiaz M, Mahmood A, Javed S, Jung SK (2019) Handcrafted and deep trackers: recent visual object tracking approaches and trends. ACM Comput Surv 52(2):43

    Article  Google Scholar 

  14. Fu C, Carrio A, Olivares-Mendez MA, Suarez-Fernandez R, Campoy P (2014) Robust real-time vision-based aircraft tracking from unmanned aerial vehicles. In: Proceedings of the IEEE international conference on robotics and automation, IEEE, pp 5441–5446

  15. Fu C, He Y, Lin F, Xiong W (2020) Robust multi-kernelized correlators for UAV tracking with adaptive context analysis and dynamic weighted filters. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04716-x

    Article  Google Scholar 

  16. Fu C, Huang Z, Li Y, Duan R, Lu P (2019) Boundary effect-aware visual tracking for UAV with online enhanced background learning and multi-frame consensus verification. arXiv preprint arXiv:1908.03701

  17. Fu C, Lin F, Li Y, Chen G (2019) Correlation filter-based visual tracking for UAV with online multi-feature learning. Remote Sens 11(5):549

    Article  Google Scholar 

  18. Fu C, Xiong W, Lin F, Yue Y (2020) Surrounding-aware correlation filter for UAV tracking with selective spatial regularization. Signal Process 167:107324

    Article  Google Scholar 

  19. Fu C, Zhang Y, Huang Z, Duan R, Xie Z (2019) Part-based background-aware tracking for UAV with convolutional features. IEEE Access 7:79997–80010

    Article  Google Scholar 

  20. Gschwindt M, Camci E, Bonatti R, Wang W, Kayacan E, Scherer S (2019) Can a robot become a movie director? learning artistic principles for aerial cinematography. arXiv preprint arXiv:1904.02579

  21. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the European conference on computer vision, pp 702–715

  22. Henriques JF, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  23. Huang Z, Fu C, Li Y, Lin F, Lu P (2019) Learning aberrance repressed correlation filters for real-time UAV tracking. In: Proceedings of the IEEE international conference on computer vision, pp 2891–2900

  24. Kiani Galoogahi H, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 1135–1143

  25. Li F, Tian C, Zuo W, Zhang L, Yang MH (2018) Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4904–4913

  26. Li P, Wang D, Wang L, Lu H (2018) Deep visual tracking: review and experimental comparison. Pattern Recogn 76:323–338

    Article  Google Scholar 

  27. Li Y, Fu C, Ding F, Huang Z, Lu G (2020) Autotrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization. arXiv preprint arXiv:2003.12949

  28. Li Y, Fu C, Ding F, Huang Z, Pan J (2019) Augmented memory for correlation filters in real-time UAV tracking. arXiv preprint arXiv:1909.10989

  29. Li Y, Fu C, Huang Z, Zhang Y, Pan J (2020) Intermittent contextual learning for keyfilter-aware UAV object tracking using deep convolutional feature. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2020.2990064

    Article  Google Scholar 

  30. Li Y, Liu G (2016) Learning a scale-and-rotation correlation filter for robust visual tracking. In: Proceedings of the IEEE international conference on image processing, IEEE, pp 454–458

  31. Li Y, Zhu J, Hoi SC, Song W, Wang Z, Liu H (2019) Robust estimation of similarity transformation for visual object tracking. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8666–8673

  32. Liu S, Wang S, Shi W, Liu H, Li Z, Mao T (2019) Vehicle tracking by detection in UAV aerial video. Sci China Inf Sci 62(2):24101

    Article  Google Scholar 

  33. Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1396–1404

  34. Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4293–4302

  35. Nebehay G, Pflugfelder R (2014) Consensus-based matching and tracking of keypoints for object tracking. In: Proceedings of the IEEE winter conference on applications of computer vision, IEEE, pp 862–869

  36. Pan Z, Liu S, Fu W (2017) A review of visual moving target tracking. Multimed Tools Appl 76(16):16989–17018

    Article  Google Scholar 

  37. Ravichandran G, Casasent D (1994) Advanced in-plane rotation-invariant correlation filters. IEEE Trans Pattern Anal Mach Intell 16(4):415–420

    Article  Google Scholar 

  38. Song W, Zhu J, Li Y, Chen C (2015) Image alignment by online robust PCA via stochastic gradient descent. IEEE Trans Circuits and Syst Video Technol 26(7):1241–1250

    Article  Google Scholar 

  39. Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PH (2017) 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

  40. Wang M, Liu Y, Huang Z (2017) Large margin object tracking with circulant feature maps. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4021–4029

  41. Wang N, Zhou W, Tian Q, Hong R, Wang M, Li H (2018) Multi-cue correlation filters for robust visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4844–4853

  42. Wang T, Qin R, Chen Y, Snoussi H, Choi C (2019) A reinforcement learning approach for UAV target searching and tracking. Multimed Tools Appl 78(4):4347–4364

    Article  Google Scholar 

  43. Wang Y, Luo X, Ding L, Fu S, Hu S (2018) Collaborative model based uav tracking via local kernel feature. Appl Soft Comput 72:90–107

    Article  Google Scholar 

  44. Yuan D, Li X, He Z, Liu Q, Lu S (2020) Visual object tracking with adaptive structural convolutional network. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2020.105554

    Article  Google Scholar 

  45. Zhang M, Xing J, Gao J, Shi X, Wang Q, Hu W (2015) Joint scale-spatial correlation tracking with adaptive rotation estimation. In: Proceedings of the IEEE international conference on computer vision workshops, pp 32–40

  46. Zhang P, Guo Q, Feng W (2019) Fast and object-adaptive spatial regularization for correlation filters based tracking. Neurocomputing 337:129–143

    Article  Google Scholar 

  47. Zhang T, Xu C, Yang MH (2017) Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4335–4343

  48. Zokai S, Wolberg G (2005) Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations. IEEE Trans Image Process 14(10):1422–1434

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61703196 and the Key Science Foundation of Zhangzhou City under Grant ZZ2019ZD11.

Author information

Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Zhangzhou, 363000, China

    Tian Li, Feifei Ding & Wenyuan Yang

  2. Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou, 363000, China

    Tian Li & Wenyuan Yang

Authors
  1. Tian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feifei Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenyuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenyuan Yang.

Ethics declarations

Conflict of interest

All authors declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, T., Ding, F. & Yang, W. UAV object tracking by background cues and aberrances response suppression mechanism. Neural Comput & Applic 33, 3347–3361 (2021). https://doi.org/10.1007/s00521-020-05200-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05200-2

Keywords

Search

Navigation