Skip to main content
SpringerLink
Log in

Underwater Moving Target Detection Based on Image Enhancement

  • Conference paper
  • First Online:
Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

Included in the following conference series:

Abstract

Motion detection in underwater video scenes is very important for many underwater computer vision tasks, such as target location, recognition and tracking. However, due to the strong optical attenuation and light scattering in water, underwater images are essentially characterized by their poor visibility, especially the low contrast and distorted information. To solve these situations, underwater moving target detection algorithm based on image enhancement is presented. The algorithm improves the contrast and clarity of the target by an adaptive underwater color image enhancement, and then extracts the moving targets by using ViBe background model. Experimental results show that the proposed algorithm can effectively extract the complete moving target by overcoming the impact of underwater environment.

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

Similar content being viewed by others

References

  1. Shen, J., Fan, T., Tang, M., Zhang, Q., Sun, Z., Huang, F.: A biological hierarchical model based underwater moving object detection. Comput. Math. Methods Med. 2014, 1–8 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  2. Lei, F., Huang, W., Zhang, Z.: Underwater moving object detection based on codebook model. In: Fifth International Conference on Computational and Information Sciences, pp. 1319–1322 (2013)

    Google Scholar 

  3. Moscheni, F., Bhattacharjee, S., Kunt, M.: Spatialtemporal segmentation based on region merging. IEEE Trans. Pattern Anal. Mach. Intell. 20(9), 897–915 (1998)

    Article  Google Scholar 

  4. Negahdaripour, S.: Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(9), 961–979 (1998)

    Article  Google Scholar 

  5. Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)

    Article  Google Scholar 

  6. Barnich, O., Droogenbroeck, M.V.: ViBE: a powerful random technique to estimate the background in video sequences. In: IEEE International Conference on Acoustics, Speech and Signal Processing -Proceedings, pp. 945–948. Taipei, Taiwan (2009)

    Google Scholar 

  7. Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)

    Article  MathSciNet  Google Scholar 

  8. Zhou, Y., Li, Q., Huo, G.: Human visual system based automatic underwater image enhancement in NSCT domain. KSII Trans. Internet Inf. Syst. 10(2), 837–856 (2016)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 41306089), the Natural Science Foundation of Jiangsu Province (No. BK20130240) and Changzhou Science and Technology Program (No. CJ20160055).

Author information

Authors and Affiliations

  1. College of Internet of Things Engineering, Hohai University, Changzhou, 213022, China

    Yan Zhou, Qingwu Li & Guanying Huo

  2. Key Laboratory of Sensor Networks and Environmental Sensing, Changzhou, 213022, China

    Yan Zhou, Qingwu Li & Guanying Huo

Authors
  1. Yan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingwu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guanying Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Zhou .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zhou, Y., Li, Q., Huo, G. (2017). Underwater Moving Target Detection Based on Image Enhancement. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59081-3_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation