Skip to main content
SpringerLink
Log in

SFD-SR: strengthened feature description and stabilized re-detection-based infrared small–dim target tracking algorithm

  • Mathematical methods in data science
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Infrared small and dim target has posed great challenges on the target detection and tracking, especially for early warning, navigation and guidance. However, most common tracking algorithms are inefficient for infrared small target due to huge computation, and complicated feature analysis on such target is useless and needless. As such, a fast and useful algorithm based on the kernel correlation filter (KCF) is proposed in this paper. It supplies a solution for target without clear characteristic, target missing and analog interference in complex backgrounds. The proposed algorithm focuses on model update and scale update rules, fusion feature expression and multi-cues constrained re-detection module; all of the ideas aim to optimize process and improve practicability. Firstly, the improved ridge regression model with solving boundary effect and model consistency can get more precise results. Secondly, a useful strengthened feature description (SFD) including the fused feature extraction and complete confidence indicator is proposed to improve the reliability of tracking. Thirdly, the stabilized re-detection (SR) strategy is designed based on motion rules, which can cope with abnormal disturbance and missing states. Fourthly, an adaptive model update is proposed, which can reduce the error accumulation. The experiment results and the comparative studies with some common algorithms show that the proposed algorithm achieves the success rate of tracking above 98%, RMSE of center location below 5 pixels and speed above 120 fps and tends to be more effective.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

Data openly available in a public repository. The data that support the findings of this study are openly available at https://doi.org/10.11922/sciencedb.902.

References

  • Bay T. T. Herbert, Ess Andreas, Gool L. V (2008) “Speeded-up robust features (surf),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, similarity Matching in Computer Vision and Multimedia

  • Bertrand NP, Lee J, Prussing KF, Shapero S, Rozell CJ (2021) Infrared search and track with unbalanced optimal transport dynamics regularization. IEEE Geosci Remote Sens Lett 18(12):2072–2076

    Article  Google Scholar 

  • Cai F, Cui J (2014) Moving target tracking based on kalman algorithm. J Eng Sci Technol Rev 7:148–153

    Article  Google Scholar 

  • Cao C, Hou Q, Gulliver TA, Lan Q (2020) A passive detection algorithm for low-altitude small target based on a wavelet neural network. Soft Comput 24:07

    Article  Google Scholar 

  • Chen Z, Tian M, Bo Y, Ling X (2019) Infrared small target detection and tracking algorithm based on new closed-loop control particle filter. Proc Inst Mech Eng Part G: J Aerosp Eng 233(4):1435–1456

    Article  Google Scholar 

  • Danelljan M, Häger G, Khan FS, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575

  • Ding L, Xu X, Cao Y, Zhai G, Yang F, Qian L (2021) Detection and tracking of infrared small target by jointly using ssd and pipeline filter. Digit Signal Process 110:102949

    Article  Google Scholar 

  • Feng W, Han R, Guo Q, Zhu J, Wang S (2019) Dynamic saliency-aware regularization for correlation filter-based object tracking. IEEE Trans Image Process 28(7):3232–3245

    Article  MathSciNet  Google Scholar 

  • Gong L, Wang C (2019) Research on moving target tracking based on fdrig optical flow. Symmetry 11(9)

  • Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. In: International Conference on Computer Vision 2011:263–270

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Least-squares R, Rifkin R, Yeo G, Poggio T (2003) Regularized least-squares classification. Advances in Learning Theory: Methods, Model and Applications, NATO Science Series III: Computer and Systems Sciences 190:06

    Google Scholar 

  • Li Y, Zhu J (2015) A scale adaptive kernel correlation filter tracker with feature integration. ECCV Workshops 03:254–265

    Google Scholar 

  • Li Z, Chen C, Liu D, Zhang C, Zeng J, Luo Z (2020) Infrared small target tracking algorithm based on temporal-spatial structure sparse bayesian estimation. Infrared Phys Technol 105:103160

    Article  Google Scholar 

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

  • Liu R, Li X, Han L, Meng J (2013) Track infrared point targets based on projection coefficient templates and non-linear correlation combined with kalman prediction. Infrared Phys Technol 57(03):68–75

    Article  Google Scholar 

  • Li Y, Zhu J, Hoi SC (2015) Reliable patch trackers: Robust visual tracking by exploiting reliable patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015:353–361

  • Mahalingam T, Subramoniam M (2020) Optimal object detection and tracking in occluded video using dnn and gravitational search algorithm. Soft Comput 24:18301–18320

    Article  Google Scholar 

  • Miaohui Zhang MX, Yang J (2013) Adaptive multi-cue based particle swarm optimization guided particle filter tracking in infrared videos. Neurocomputing 122:163–171 (advances in cognitive and ubiquitous computing)

  • Nie J, Guo X (2020) A target tracking method based on improved camshift algorithm. In: 2020 IEEE 5th information technology and mechatronics engineering conference (ITOEC), pp 254–258

  • Qian K, Zhou H, Rong S, Wang B, Cheng K (2017) Infrared dim-small target tracking via singular value decomposition and improved kernelized correlation filter. Infrared Phys Technol 82:18–27

    Article  Google Scholar 

  • Qian K, Rong SH, Cheng KH (2021) Anti-interference small target tracking from infrared dual waveband imagery. Infrared Phys Technol 118:103882

    Article  Google Scholar 

  • Vojir T, Noskova J, Matas J (2014) Robust scale-adaptive mean-shift for tracking. Pattern Recogn Lett 49:250–258

    Article  Google Scholar 

  • Wan M, Ye X, Zhang X, Xu Y, Gu G, Chen Q (2022) Infrared small target tracking via gaussian curvature-based compressive convolution feature extraction. IEEE Geosci Remote Sens Lett 19:1–5

    Google Scholar 

  • Wang X, Ning C, Xu L (2015) Spatiotemporal difference-of-gaussians filters for robust infrared small target tracking in various complex scenes. Appl Opt 54(7):1573–1586

  • Wang W, Wang C, Liu S, Zhang T, Cao X (2018) Robust target tracking by online random forests and superpixels. IEEE Trans Circ Syst Video Technol 28(7):1609–1622

    Article  Google Scholar 

  • Xiao S, Ma Y, Fan F, Huang J, Wu M (2020) Tracking small targets in infrared image sequences under complex environmental conditions. Infrared Phys Technol 104:103102

    Article  Google Scholar 

  • Xu T, Feng Z-H, Wu X-J, Kittler J (2019) Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking. IEEE Trans Image Process 28(11):5596–5609

    Article  MathSciNet  Google Scholar 

  • Yang X, Li S, Yu J, Zhang K, Yang J, Yan J (2021) Gf-kcf: Aerial infrared target tracking algorithm based on kernel correlation filters under complex interference environment. Infrared Phys Technol 119:103958

    Article  Google Scholar 

  • Zeng Y-L, Lan J-H, Zou J-L (2017) Research on infrared dim-small target tracking algorithm with template sliding confidence constraint. Binggong Xuebao/Acta Armamentarii 38(09):1771–1778

    Google Scholar 

  • Zhang XYSZS, Sui Y, Zhang L (2015) Hybrid support vector machines for robust object tracking. Pattern Recogn 48(8):2474–2488

    Article  Google Scholar 

  • Zhang X, Ren K, Wan M, Gu G, Chen Q (2017) Infrared small target tracking based on sample constrained particle filtering and sparse representation. Infrared Phys Technol 87:72–82

    Article  Google Scholar 

Download references

Funding

No fnding support.

Author information

Authors and Affiliations

  1. Department of Automation, Rocket Force University of Engineering, X’an, 710025, China

    Zehao Li, Shouyi Liao & Tong Zhao

  2. Department of Automation, National University of Defense Technology, Changsha, 410000, China

    Meiping Wu

Authors
  1. Zehao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shouyi Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meiping Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zehao Li.

Ethics declarations

Conflict of interest

All of the authors declare that they have no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Liao, S., Wu, M. et al. SFD-SR: strengthened feature description and stabilized re-detection-based infrared small–dim target tracking algorithm. Soft Comput 28, 963–979 (2024). https://doi.org/10.1007/s00500-023-09307-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09307-1

Keywords

Search

Navigation