Skip to main content
SpringerLink
Log in

Analyzing Underwater Videos for Fish Detection, Counting and Classification

  • Conference paper
  • First Online:
Computational Vision and Bio-Inspired Computing ( ICCVBIC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1108))

Abstract

Underwater video processing is a valuable tool for analyzing the presence and behaviour of fishes in underwater. Video based analysis of fishes finds its use in aquaculture, fisheries and protection of fishes in oceans. This paper proposes a system to detect, count, track and classify the fishes in underwater videos. In the proposed approach, two systems are developed, a counting system, which uses gaussian mixture model (for foreground detection), morphological operations (for denoising), blob analysis (for counting) and kalman filtering (for tracking), and a classification system, which uses bag of features approach that is used to classify the fishes. In the bag of feature approach, surf features are extracted from the images to obtain feature descriptors. k-mean clustering is applied on the feature descriptors, to obtain visual vocabulary. The test features are input to the MSVM classifier, which uses visual vocabulary to classify the images. The proposed system achieves an average accuracy of 90% in counting and 88.9% in classification, respectively.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Boudhane, M., Nsiri, B.: Underwater image processing method for fish localization and detection in submarine environment. J. Vis. Commun. Image Represent. 39, 226–238 (2016)

    Article  Google Scholar 

  2. Spampinato, C., et al.: Detecting, tracking and counting fish in low quality unconstrained underwater videos. VISAPP 2, 514–519 (2008)

    Google Scholar 

  3. Prabowo, M.R., Hudayani, N., et al.: A moving objects detection in underwater video using subtraction of the background model. In: 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, pp. 1–4 (2017). https://doi.org/10.1109/eecsi.2017.8239148

  4. Palazzo, S., Kavasidis, I., Spampinato, C.: Covariance based modeling of underwater scenes for fish detection. In: 2013 IEEE International Conference on Image Processing. IEEE (2013)

    Google Scholar 

  5. Rathi, D., Jain, S., Indu, S.: Underwater fish species classification using convolutional neural network and deep learning. In: 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). IEEE (2017)

    Google Scholar 

  6. Shortis, M., Ravanbakhsh, M., et al.: An application of shape-based level sets to fish detection in underwater ımages. In: GSR (2014)

    Google Scholar 

  7. Fabic, J.N., et al.: Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. In: 2013 IEEE International on Underwater Technology Symposium (UT). IEEE (2013). In Proceedings International Conference on Communication and Signal Processing, ICCSP 2017 (2017)

    Google Scholar 

  8. Ghani, A.S.A., Isa, N.A.M.: Enhancement of low quality underwater image through integrated global and local contrast correction. Appl. Soft Comput. 37, 332–344 (2015)

    Article  Google Scholar 

  9. Benson, B., et al.: Field programmable gate array (FPGA) based fish detection using Haar classifiers. American Academy of Underwater Sciences (2009)

    Google Scholar 

  10. Prabhakar, P., Kumar, P.: Underwater image denoising using adaptive wavelet subband thresholding. In: Proceedings IEEE ICSIP 2010, Chennai, India, pp. 322–327 (2010)

    Google Scholar 

  11. Feifei, S., Xuemeng, Z., Guoyu, W.: An approach for underwater image denoising svia wavelet decomposition and high-pass filter. In: Proceedings IEEE ICICTA 2011, Shenzhen, China, pp. 417–420 (2011)

    Google Scholar 

  12. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)

    Article  Google Scholar 

  13. Evans, F.H.: Detecting fish in underwater video using the EM algorithm. In: Proceedings of the 2003 International Conference on Image Processing (ICIP), vol. 3, p. III–1029. IEEE (2003)

    Google Scholar 

  14. Khanfar, H., et al.: Automatic Fish Counting in Underwater Video Cuenta Automática de Peces en Video Submarino Comptage Automatique de Poisson dans la Vidéo Sous-Marine. Preprocessing. Indian Journal of Science and Technology, pp. 1170–1175 (2014)

    Google Scholar 

  15. Singhal, N., Singhal, N., Kalaichelvi, V.: Image classification using bag of visual words model with FAST and FREAK. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, pp. 1–5 (2017)

    Google Scholar 

  16. Aarthi, R., Arunkumar, C., RagheshKrishnan, K.: Automatic isolation and classification of vehicles in a traffic video. In: 2011 World Congress on Information and Communication Technologies, Mumbai, pp. 357–361 (2011)

    Google Scholar 

  17. Venkataraman, D., Mangayarkarasi, N.: Computer vision based feature extraction of leaves for identification of medicinal values of plants. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE (2016)

    Google Scholar 

  18. https://www.imageclef.org/lifeclef/2015/fish

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidhyapeetham, Coimbatore, India

    G. Durga Lakshmi & K. Raghesh Krishnan

Authors
  1. G. Durga Lakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Raghesh Krishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Durga Lakshmi .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Faculty of Engineering, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Abdullah M. Iliyasu

Ethics declarations

✓ All authors declare that there is no conflict of interest

✓ No humans/animals involved in this research work.

✓ We have used our own data.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lakshmi, G.D., Krishnan, K.R. (2020). Analyzing Underwater Videos for Fish Detection, Counting and Classification. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_49

Download citation

Publish with us

Policies and ethics

Search

Navigation