Fish identification from videos captured in uncontrolled underwater environments

Faisal Shafait, Ajmal Mian, Mark Shortis, Bernard Ghanem, Phil F. Culverhouse, Duane Edgington, Danelle Cline, Mehdi Ravanbakhsh, James Seager, Euan S. Harvey

Research output: Contribution to journalArticlepeer-review


There is an urgent need for the development of sampling techniques which can provide accurate and precise count, size, and biomass data for fish. This information is essential to support the decision-making processes of fisheries and marine conservation managers and scientists. Digital video technology is rapidly improving, and it is now possible to record long periods of high resolution digital imagery cost effectively, making single or stereo-video systems one of the primary sampling tools. However, manual species identification, counting, and measuring of fish in stereo-video images is labour intensive and is the major disincentive against the uptake of this technology. Automating species identification using technologies developed by researchers in computer vision and machine learning would transform marine science. In this article, a new paradigm of image set classification is presented that can be used to achieve improved recognition rates for a number of fish species. State-of-the-art image set construction, modelling, and matching algorithms from computer vision literature are discussed with an analysis of their application for automatic fish species identification. It is demonstrated that these algorithms have the potential of solving the automatic fish species identification problem in underwater videos captured within unconstrained environments.

Original languageEnglish (US)
Pages (from-to)2737-2746
Number of pages10
JournalICES Journal of Marine Science
Issue number10
StatePublished - Nov 2016

Bibliographical note

KAUST Repository Item: Exported on 2021-07-08
Acknowledgements: We would like to thank the anonymous reviewers whose detailed and insightful comments have helped us in bringing this manuscript to its current form. In addition, the authors acknowledge support from the Australian Research Council Grant LP110201008, which provided the primary funding for this study in addition to UWA Research Collaboration Award (RCA) grant and King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research. A.M. was supported by the Australian Research Council Fellowship DP110102399.


  • computer vision
  • fish classification
  • fish identification
  • image analysis
  • image sets
  • species recognition

ASJC Scopus subject areas

  • Ecology
  • Ecology, Evolution, Behavior and Systematics
  • Oceanography
  • Aquatic Science


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