Fish Monitoring and Sizing Using Computer Vision

  • Alvaro RodriguezEmail author
  • Angel J. Rico-Diaz
  • Juan R. Rabuñal
  • Jeronimo Puertas
  • Luis Pena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)


This paper proposes an image processing algorithm, based in a non invasive 3D optical stereo system and the use of computer vision techniques, to study fish in fish tanks or pools.

The proposed technique will allow to study biological variables of different fish species in underwater environments.

This knowledge, may be used to replace traditional techniques such as direct observation, which are impractical or affect the fish behavior, in task such as aquarium and fish farm management or fishway evaluation.

The accuracy and performance of the proposed technique has been tested, conducting different assays with living fishes, where promising results were obtained.


Segmentation Computer vision 


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  1. 1.
    Leon-Santana, M., Hernandez, J.M.: Optimum management and environmental protection in the aquaculture industry. Ecological Economics 64, 849–857 (2008)CrossRefGoogle Scholar
  2. 2.
    Cappo, M., Harvey, E., Malcolm, H., Speare, P.: Potential of video techniques to monitor diversity, abundance and size of fish in studies of marine protected areas. In: Aquatic Protected Areas-What Works Best and How do We Know, pp. 455–464 (2003)Google Scholar
  3. 3.
    Brosnan, T., Sun, D.-W.: Inspection and grading of agricultural and food products by computer vision systems a review. Computers and Electronics in Agriculture 36, 193–213 (2002)CrossRefGoogle Scholar
  4. 4.
    Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sun, D.-W., Menesatti, P.: Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision. Food and Bioprocess Technology 4, 673–692 (2011)CrossRefGoogle Scholar
  5. 5.
    Armstrong, J.D., Bagley, P.M., Priede, I.G.: Photographic and acoustic tracking observations of the behavior of the grenadier Coryphaenoides (Nematonorus) armatus, the eel Synaphobranchus bathybius, and other abyssal demersal fish in the North Atlantic Ocean. Marine Biology 112, 1432–1793 (1992)CrossRefGoogle Scholar
  6. 6.
    Steig, T.W., Iverson, T.K.: Acoustic monitoring of salmonid density, target strength, and trajectories at two dams on the Columbia River, using a split-beam scaning system. Fisheries Research 35, 43–53 (1998)CrossRefGoogle Scholar
  7. 7.
    Rodriguez, A., Bermudez, M., Rabuñal, J., Puertas, J.: Fish tracking in vertical slot fishways using computer vision techniques. Journal of Hydroinformatics (2014)Google Scholar
  8. 8.
    Zion, B., Shklyar, A., Karplus, I.: Sorting fish by computer vision. Computers and Electronics in Agriculture 23, 175–187 (1999)CrossRefGoogle Scholar
  9. 9.
    Zion, B., Shklyar, A., Karplus, I.: In-vivo fish sorting by computer vision. Aquacultural Engineering 22, 165–179 (2000)CrossRefGoogle Scholar
  10. 10.
    Petrell, R.J., Shi, X., Ward, R.K., Naiberg, A., Savage, C.R.: Determining fish size and swimming speed in cages and tanks using simple video techniques. Aquacultural Engineering 16, 63–84 (1997)CrossRefGoogle Scholar
  11. 11.
    Israeli, D., Kimmel, E.: Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision. Aquacultural Engineering 15, 423–440 (1996)CrossRefGoogle Scholar
  12. 12.
    Ruff, B.P., Marchant, J.A., Frost, A.R.: Fish sizing and monitoring using a stereo image analysis system applied to fish farming. Aquacultural Engineering 14, 155–173 (1995)CrossRefGoogle Scholar
  13. 13.
    Duarte, S., Reig, L., Oca, J., Flos, R.: Computerized imaging techniques for fish tracking in behavioral studies. European Aquaculture Society (2004)Google Scholar
  14. 14.
    Chambah, M., Semani, D., Renouf, A., Courtellemont, P., Rizzi, A.: Underwater color constancy enhancement of automatic live fish recognition. In: IS&T Electronic Imaging (SPIE) (2004)Google Scholar
  15. 15.
    Morais, E.F., Campos, M.F.M., Padua, F.L.C., Carceroni, R.L.: Particle filter-based predictive tracking for robust fish count. In: Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI) (2005)Google Scholar
  16. 16.
    Clausen, S., Greiner, K., Andersen, O., Lie, K.-A., Schulerud, H., Kavli, T.: Automatic segmentation of overlapping fish using shape priors. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 11–20. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Chuang, M.-C., Hwang, J.-N., Williams, K., Towler, R.: Automatic fish segmentation via double local thresholding for trawl-based underwater camera systems. In: IEEE International Conference on Image Processing (ICIP) (2011)Google Scholar
  18. 18.
    Spampinato, C., Chen-Burger, Y.-H., Nadarajan, G., Fisher, R.: Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos. In: Int. Conf. on Computer Vision Theory and Applications (VISAPP) (2008)Google Scholar
  19. 19.
    Lines, J.A., Tillett, R.D., Ross, L.G., Chan, D., Hockaday, S., McFarlane, N.J.B.: An automatic image-based system for estimating the mass of free-swimming fish. Computers and Electronics in Agriculture 31, 151–168 (2001)CrossRefGoogle Scholar
  20. 20.
    Frenkel, V., Kindschi, G., Zohar, Y.: Noninvasive, mass marking of fish by immersion in calcein: evaluation of fish size and ultrasound exposure on mark endurance. Aquaculture 214, 169–183 (2002)CrossRefGoogle Scholar
  21. 21.
    Martinez-de Dios, J., Serna, C., Ollero, A.: Computer vision and robotics techniques in fish farms. Robotica 21, 233–243 (2003)CrossRefGoogle Scholar
  22. 22.
    White, D.J., Svellingen, C., Strachan, N.J.C.: Automated measurement of species and length of fish by computer vision. Fisheries Research 80, 203–210 (2006)CrossRefGoogle Scholar
  23. 23.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry. Cambridge University Press (2004)Google Scholar
  24. 24.
    Abad, F.H., Abad, V.H., Andreu, J.F., Vives, M.O.: Application of Projective Geometry to Synthetic Cameras. In: XIV International Conference of Graphic Engineering (2002)Google Scholar
  25. 25.
    Martin, N., Perez, B.A., Aguilera, D.G., Lahoz, J.G.: Applied Analysis of Camera Calibration Methods for Photometric Uses. In: VII National Conference of Topography and Cartography (2004)Google Scholar
  26. 26.
    Zhang, Z.: Flexible Camera Calibration By Viewing a Plane From Unknown Orientations. In: International Conference on Computer Vision (ICCV) (1999)Google Scholar
  27. 27.
    OPENCV: Open Source Computer Vision, (Visited: February 2015)
  28. 28.
    Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies 6, 271–288 (1998)CrossRefGoogle Scholar
  29. 29.
    Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE ICCV, pp. 1–19 (1999)Google Scholar
  30. 30.
    KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Video-Based Surveillance Systems, pp. 135–144. Springer (2002)Google Scholar
  31. 31.
    Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: International Conference on Patern Recognition (ICPR 2004), pp. 28–31 (2004)Google Scholar
  32. 32.
    Godbehere, A.B., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: American Control Conference (ACC), pp. 4305–4312 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alvaro Rodriguez
    • 1
    Email author
  • Angel J. Rico-Diaz
    • 1
    • 2
  • Juan R. Rabuñal
    • 1
    • 2
  • Jeronimo Puertas
    • 3
  • Luis Pena
    • 3
  1. 1.Department of Information and Communications TechnologiesUniversity of A CoruñaA CoruñaSpain
  2. 2.Centre of Technological Innovation in Construction and Civil Engineering (CITEEC)University of A CoruñaA CoruñaSpain
  3. 3.Department of Hydraulic Engineering (ETSECCP)University of A CoruñaA CoruñaSpain

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