Skip to main content
SpringerLink
Log in

Detection of Multiple Small Moving Targets Against Complex Ground Background

  • Conference paper
  • First Online:
Wireless Mobile Communication and Healthcare (MobiHealth 2021)

  • 399 Accesses

Abstract

To tackle the problem that it is difficult to detect small moving targets accurately against complex ground background, a target detection algorithm that combines target motion information and trajectory association is proposed. To tackle the problem of small target size, firstly, background motion compensation is performed to obtain the background motion parameters. Then, forward and backward motion history maps are calculated to fuse continuous difference images for enhanced motion information of small targets. Finally, morphology processing is used to obtain the area of small moving targets. To tackle the problem of complex background, the Kalman predictor is used to predict the target position, and the Hungarian matching algorithm is used to correlate targets to obtain the target trajectory. Then, based on the target trajectory, targets missed by detection are supplemented to improve the target recall rate and false alarm targets are filtered out to improve the target precision rate. Experimental results show that the proposed algorithm has good detection performance, with the recall rate higher than 93%, the precision rate higher than 92%, and the F-measure higher than 93%.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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. Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–43. IEEE (2012)

    Google Scholar 

  2. Siam, M., ElSayed, R., ElHelw, M.: On-board multiple target detection and tracking on camera-equipped aerial vehicles. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2399–2405. IEEE (2012)

    Google Scholar 

  3. Moo Yi, K., Yun, K., Wan Kim, S., Jin Chang, H., Young Choi, J.: Detection of moving objects with non-stationary cameras in 5.8 ms: bringing motion detection to your mobile device. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–34 (2013)

    Google Scholar 

  4. Shen, H., Li, S., Zhu, C., Chang, H., Zhang, J.: Moving object detection in aerial video based on spatiotemporal saliency. Chin. J. Aeronaut. 26(5), 1211–1217 (2013)

    Article  Google Scholar 

  5. Shakeri, M., Zhang, H.: Detection of small moving targets using a moving camera. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 2777–2782. IEEE (2014)

    Google Scholar 

  6. Sadeghi-Tehran, P., Clarke, C., Angelov, P.: A real-time approach for autonomous detection and tracking of moving objects from UAV. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 43–49. IEEE (2014)

    Google Scholar 

  7. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: 2011 International Conference on Computer Vision, pp. 2548–2555. IEEE (2011)

    Google Scholar 

  8. Wang, Z., Wang, C., Huang, F., Liu, J.: Based on spatial-temporal multiframe association infrared target detection. In: MIPPR 2015: Automatic Target Recognition and Navigation, vol. 9812, p. 98121A. International Society for Optics and Photonics (2015)

    Google Scholar 

  9. Wei, L., Weijie, Z., Cheng, L., Zhonglin, X., Kaiqiao, T.: Small moving object detection based on the improved ORB feature matching method. Optoelectron. Eng. 42(10), 13–20 (2015)

    Google Scholar 

  10. Rozantsev, A., Lepetit, V., Fua, P.: Flying objects detection from a single moving camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4128–4136 (2015)

    Google Scholar 

  11. Rozantsev, A., Lepetit, V., Fua, P.: Detecting flying objects using a single moving camera. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 879–892 (2016)

    Article  Google Scholar 

  12. Junhua, Y., He, D., Qianqian, X., Yong, Y.: Slowly moving small object detection in complex moving background based on adaptive threshold segmentation. Electron. Des. Eng. 24(06), 77–80+84 (2016)

    Google Scholar 

  13. Meng, Y., Yan, C.: Detection and tracking of small moving objects based on multi-view aerial photography registration. Comput. Eng. Appl. 52(14), 27–31 (2016)

    Google Scholar 

  14. Yang, T., et al.: Small moving vehicle detection in a satellite video of an urban area. Sensors 16(9), 1528 (2016)

    Article  Google Scholar 

  15. Li, Y., Zhang, Y., Yu, J.G., Tan, Y., Tian, J., Ma, J.: A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences. Inf. Sci. 369, 548–563 (2016)

    Article  MathSciNet  Google Scholar 

  16. Lou, J., Zhu, W., Wang, H., Ren, M.: Small target detection combining regional stability and saliency in a color image. Multimed. Tools. Appl. 76(13), 14781–14798 (2017).

    Google Scholar 

  17. Yan, J., Xu, Q., Duan, H., Yang, Y., Xiao, Y.: Slow ground moving object detection in rotating complex background. J. Imaging Sci. Technol. 61(2), 20507–20511 (2017)

    Article  Google Scholar 

  18. Gao, J., Wen, C., Liu, M.: Robust small target co-detection from airborne infrared image sequences. Sensors 17(10), 2242 (2017)

    Article  Google Scholar 

  19. Zhang, Z., Cao, Y., Ding, M., Zhuang, L., Wang, Z.: Spatial and temporal context information fusion based flying objects detection for autonomous sense and avoid. In: 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 569–578. IEEE (2018)

    Google Scholar 

  20. Yan, D., Sun, W.: Small moving object detection based on sequence confidence method in UAV video. In: Krömer, P., Zhang, H., Liang, Y., Pan, J.-S. (eds.) ECC 2018. AISC, vol. 891, pp. 676–683. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-03766-6_76

    Chapter  Google Scholar 

  21. Yi, X., Wang, B., Zhou, H., Qin, H.: Dim and small infrared target fast detection guided by visual saliency. Infrared Phys. Technol. 97, 6–14 (2019)

    Article  Google Scholar 

  22. Lucas, B.D.: An iterative image registoration techniques with an application to stereo vision. Proc. IJCAI 81(3), 674–679 (1981)

    Google Scholar 

  23. Yin, Z., Collins, R.: Moving object localization in thermal imagery by forward-backward MHI. In: 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), p. 133. IEEE (2006)

    Google Scholar 

  24. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61471194 and 61705104), the Fundamental Research Funds for the Central Universities (NJ2020021, NT2020022), the Natural Science Foundation of Jiangsu Province (BK20170804), and National Defense Science and Technology Special Innovation Zone Project.

Author information

Authors and Affiliations

  1. Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, No. 29 Yudao Street, Nanjing, 210016, China

    Junhua Yan & Yin Zhang

  2. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, China

    Junhua Yan, Jingchun Qi, Xuyang Cai, Yin Zhang, Kun Zhang & Yue Ma

Authors
  1. Junhua Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingchun Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuyang Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yue Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junhua Yan .

Editor information

Editors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinbo Gao

  2. The University of Sydney, Sydney, NSW, Australia

    Abbas Jamalipour

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Lei Guo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yan, J., Qi, J., Cai, X., Zhang, Y., Zhang, K., Ma, Y. (2022). Detection of Multiple Small Moving Targets Against Complex Ground Background. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06368-8_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06367-1

  • Online ISBN: 978-3-031-06368-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation