An automatic identification and monitoring system for coral reef fish

Joseph Wilder, Chetan Tonde, Ganesh Sundar, Ning Huang, Lev Barinov, Jigesh Baxi, James Bibby, Andrew Rapport, Edward Pavoni, Serena Tsang, Eri Garcia, Felix Mateo, Tanya M. Lubansky, Gareth J. Russell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


To help gauge the health of coral reef ecosystems, we developed a prototype of an underwater camera module to automatically census reef fish populations. Recognition challenges include pose and lighting variations, complicated backgrounds, within-species color variations and within-family similarities among species. An open frame holds two cameras, LED lights, and two 'background' panels in an L-shaped configuration. High-resolution cameras send sequences of 300 synchronized image pairs at 10 fps to an on-shore PC. Approximately 200 sequences containing fish were recorded at the New York Aquarium's Glover's Reef exhibit. These contained eight 'common' species with 85-672 images, and eight 'rare' species with 5-27 images that were grouped into an 'unknown/rare' category for classification. Image pre-processing included background modeling and subtraction, and tracking of fish across frames for depth estimation, pose correction, scaling, and disambiguation of overlapping fish. Shape features were obtained from PCA analysis of perimeter points, color features from opponent color histograms, and 'banding' features from DCT of vertical projections. Images were classified to species using feedforward neural networks arranged in a three-level hierarchy in which errors remaining after each level are targeted by networks in the level below. Networks were trained and tested on independent image sets. Overall accuracy of species-specific identifications typically exceeded 96% across multiple training runs. A seaworthy version of our system will allow for population censuses with high temporal resolution, and therefore improved statistical power to detect trends. A network of such devices could provide an 'early warning system' for coral ecosystem collapse.

Original languageEnglish (US)
Title of host publicationApplications of Digital Image Processing XXXV
StatePublished - 2012
EventApplications of Digital Image Processing XXXV - San Diego, CA, United States
Duration: Aug 13 2012Aug 16 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherApplications of Digital Image Processing XXXV
Country/TerritoryUnited States
CitySan Diego, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • Coral reefs
  • Fish populations
  • Image processing
  • Pattern recognition
  • Population census
  • Species recognition


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