Skip to main content
SpringerLink
Log in

An Infrared Moving Small Object Detection Method Based on Trajectory Growth

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

Included in the following conference series:

  • 1477 Accesses

Abstract

Aiming at the detection of infrared moving small objects for moving camera, an infrared moving small object detection method based on trajectory growth is proposed. In the first stage, the proposed method firstly estimates the homography between images by pairs of corresponding points obtained by pyramidal Lucas-Kanade optical flow method. Then the changed regions in the image are extracted by the three-frame difference method, and the optimized segmentation method is used to produce accurate extraction results. In the second stage, based on the assumption of short-time approximate uniform linear motion, the old trajectories are grown and the new trajectories are generated to reduce the interference of false alarm. Then, the final detection results are obtained by confirming the trajectories. Experiments on publicly available dataset show that the proposed method achieves F1 score higher than 0.91 on average, indicating it can accurately detect small objects in complex and even noisy background with low false alarm rate and high efficiency.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Srivastava, H.B., Kurnar, V., Verma, H.K., et al.: Image pre-processing algorithms for detection of small/point airborne targets. Def. Sci. J. 59(2), 166–174 (2009)

    Article  Google Scholar 

  2. Wei, Y., You, X., Li, H.: Multiscale patch-based contrast measure for small infrared target detection. Pattern Recogn. 58, 216–226 (2016)

    Article  Google Scholar 

  3. Hu, J., Yu, Y., Liu, F.: Small and dim target detection by background estimation. Infrared Phys. Technol. 73, 141–148 (2015)

    Article  Google Scholar 

  4. Qi, H., Mo, B., Liu, F., et al.: Small infrared target detection utilizing local region similarity difference map. Infrared Phys. Technol. 71, 131–139 (2015)

    Article  Google Scholar 

  5. Bai, X., Zhou, F.: Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recogn. 43(6), 2145–2156 (2010)

    Article  Google Scholar 

  6. Deng, L., Zhang, J., Xu, G., et al.: Infrared small target detection via adaptive M-estimator ring top-hat transformation. Pattern Recogn. 112, 107729 (2021)

    Article  Google Scholar 

  7. Wang, X., Peng, Z., Zhang, P., et al.: Infrared small target detection via nonnegativity-constrained variational mode decomposition. IEEE Geosci. Remote Sens. Lett. 14(10), 1700–1704 (2017)

    Article  Google Scholar 

  8. Chen, C.P., Li, H., Wei, Y., et al.: A local contrast method for small infrared target detection. IEEE Trans. Geosci. Remote Sens. 52(1), 574–581 (2014)

    Article  Google Scholar 

  9. Han, J., Ma, Y., Zhou, B., et al.: A robust infrared small target detection algorithm based on human visual system. IEEE Geosci. Remote Sens. Lett. 11(12), 2168–2172 (2014)

    Article  Google Scholar 

  10. Chen, Y., Nasrabadi, N.M., Tran, T.D.: Sparse representation for target detection in hyperspectral imagery. IEEE J. Sel. Top. Signal Process. 5(3), 629–640 (2011)

    Article  Google Scholar 

  11. Gao, C., Meng, D., Yang, Y., et al.: Infrared patch-image model for small target detection in a single image. IEEE Trans. Image Process. 22(12), 4996–5009 (2013)

    Article  MathSciNet  Google Scholar 

  12. Dai, Y., Wu, Y., Zhou, F., et al.: Attentional local contrast networks for infrared small target detection. IEEE Trans. Geosci. Remote Sens. 59(11), 9813–9824 (2021)

    Article  Google Scholar 

  13. Zhao, B., Wang, C., Fu, Q., et al.: A novel pattern for infrared small target detection with generative adversarial network. IEEE Trans. Geosci. Remote Sens. 59(5), 4481–4492 (2021)

    Article  Google Scholar 

  14. Reed, I.S., Gagliardi, R.M., Shao, H.M.: Application of three-dimensional filtering to moving target detection. IEEE Trans. Aerosp. Electron. Syst. 19(6), 898–905 (1983)

    Article  Google Scholar 

  15. Reed, I.S., Gagliardi, R.M., Stotts, L.B.: Optical moving target detection with 3-D matched filtering. IEEE Trans. Aerosp. Electron. Syst. 24(4), 327–336 (1988)

    Article  Google Scholar 

  16. Wang, B., Xu, W., Zhao, M., et al.: Antivibration pipeline-filtering algorithm for maritime small target detection. Opt. Eng. 53(11), 113109 (2014)

    Article  Google Scholar 

  17. Wang, B., Dong, L., Zhao, M., et al.: A small dim infrared maritime target detection algorithm based on local peak detection and pipeline-filtering. In: 7th International Conference on Graphic and Image Processing (ICGIP), pp. 188–193 (2015)

    Google Scholar 

  18. Dong, L., Wang, B., Zhao, M., et al.: Robust infrared maritime target detection based on visual attention and spatiotemporal filtering. IEEE Trans. Geosci. Remote Sens. 55(5), 3037–3050 (2017)

    Article  Google Scholar 

  19. Tomasi, C.: Detection and tracking of point features. Tech. Rep. 91(21), 9795–9802 (1991)

    Google Scholar 

  20. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  21. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  22. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  23. Shi, J.: Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, pp. 593–600. IEEE (1994)

    Google Scholar 

  24. Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)

    Article  Google Scholar 

  25. Hui, B., Song, Z., Fan, H., et al.: A dataset for infrared detection and tracking of dim-small aircraft targets underground/air background. China Sci. Data 5(3), 1–12 (2020)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the National Key Research and Development Program of China (No. 2019YFC1511102) and the National Natural Science Foundation of China (No. 12002215).

Author information

Authors and Affiliations

  1. College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China

    Dilong Li, Shuixin Pan, Haopeng Wang, Yueqiang Zhang, Linyu Huang, Hongxi Guo, Xiaolin Liu & Qifeng Yu

  2. Institute of Intelligent Optical Measurement and Detection, Shenzhen University, Shenzhen, 518060, China

    Dilong Li, Shuixin Pan, Haopeng Wang, Yueqiang Zhang, Linyu Huang, Hongxi Guo, Xiaolin Liu & Qifeng Yu

Authors
  1. Dilong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuixin Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haopeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yueqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Linyu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongxi Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaolin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qifeng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yueqiang Zhang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, D. et al. (2022). An Infrared Moving Small Object Detection Method Based on Trajectory Growth. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18916-6_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18915-9

  • Online ISBN: 978-3-031-18916-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation